Machine control using a predictive map

ABSTRACT

One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.

FIELD OF THE DESCRIPTION

The present description relates to agricultural machines, forestrymachines, construction machines, and turf management machines.

BACKGROUND

There are a wide variety of different types of agricultural machines.Some agricultural machines include harvesters, such as combineharvesters, sugar cane harvesters, cotton harvesters, self-propelledforage harvesters, and windrowers. Some harvesters can be fitted withdifferent types of heads to harvest different types of crops.

As a harvester travels across and completes a harvesting operation, thebyproducts from the harvesting operation, called material other thangrain (MOG), are dispersed by the agricultural harvester across thefield.

The discussion above is merely provided for general backgroundinformation and is not intended to be used as an aid in determining thescope of the claimed subject matter.

SUMMARY

One or more information maps are obtained by an agricultural workmachine. The one or more information maps map one or more agriculturalcharacteristic values at different geographic locations of a field. Anin-situ sensor on the agricultural work machine senses an agriculturalcharacteristic as the agricultural work machine moves through the field.A predictive map generator generates a predictive map that predicts apredictive agricultural characteristic at different locations in thefield based on a relationship between the values in the one or moreinformation maps and the agricultural characteristic sensed by thein-situ sensor. The predictive map can be output and used in automatedmachine control. This Summary is provided to introduce a selection ofconcepts in a simplified form that are further described below in theDetailed Description. This Summary is not intended to identify keyfeatures or essential features of the claimed subject matter, nor is itintended to be used as an aid in determining the scope of the claimedsubject matter. The claimed subject matter is not limited to examplesthat solve any or all disadvantages noted in the background.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a partial pictorial, partial schematic illustration of oneexample of a combine harvester.

FIG. 2 is a block diagram showing some portions of an agriculturalharvester in more detail, according to some examples of the presentdisclosure.

FIGS. 3A-3B (collectively referred to herein as FIG. 3 ) show a flowdiagram illustrating an example of operation of an agriculturalharvester in generating a map.

FIG. 4 is a block diagram showing one example of a predictive modelgenerator and a predictive map generator.

FIG. 5 is a flow diagram showing an example of operation of anagricultural harvester in receiving a vegetative index, moisture and/ortopographic map, detecting a residue characteristic, and generating afunctional predictive residue map for use in controlling theagricultural harvester during a harvesting operation.

FIG. 6 is a block diagram showing one example of a control zonegenerator.

FIG. 7 is a flow diagram illustrating one example of the operation ofthe control zone generator shown in FIG. 6 .

FIG. 8 illustrates a flow diagram showing an example of operation of acontrol system in selecting a target settings value to control anagricultural harvester.

FIG. 9 is a block diagram showing one example of an operator interfacecontroller.

FIG. 10 is a flow diagram illustrating one example of an operatorinterface controller.

FIG. 11 is a pictorial illustration showing one example of an operatorinterface display.

FIG. 12 is a block diagram showing one example of an agriculturalharvester in communication with a remote server environment.

FIGS. 13-15 show examples of mobile devices that can be used in anagricultural harvester.

FIG. 16 is a block diagram showing one example of a computingenvironment that can be used in an agricultural harvester.

DETAILED DESCRIPTION

For the purposes of promoting an understanding of the principles of thepresent disclosure, reference will now be made to the examplesillustrated in the drawings, and specific language will be used todescribe the same. It will nevertheless be understood that no limitationof the scope of the disclosure is intended. Any alterations and furthermodifications to the described devices, systems, methods, and anyfurther application of the principles of the present disclosure arefully contemplated as would normally occur to one skilled in the art towhich the disclosure relates. In particular, it is fully contemplatedthat the features, components, and/or steps described with respect toone example may be combined with the features, components, and/or stepsdescribed with respect to other examples of the present disclosure.

The present description relates to using in-situ data taken concurrentlywith an agricultural operation, in combination with predictive or priordata, to generate a predictive map such as a predictive residue map. Insome examples, the predictive map can be used to control an agriculturalwork machine, such as an agricultural harvester to spread residue acrossa field uniformly.

Uniform residue spreading can increase the overall production of afield. Non-uniform residue coverage can cause a variety of differentproblems. For instance, nutrients in the residue will be concentratedunder the bands of high residue. Or for instance, pests such as insects,slugs, and rodents reside in larger residue piles. Or for instance, weedseeds and grain lost through the combine will be concentrated in theresidue patches. Or for instance, herbicide effectiveness will becompromised because the herbicides are blocked from reaching the soil byresidue patches. Or for instance, windrows or piles of residue canreduce performance of a planter because the seed openers cannot cutthrough excessive residue, and seeds are not planted in the soil. Or forinstance, non-uniform residue coverage can also cause non-uniform soiltemperature and moisture conditions. The soil under areas of moreresidue will be several degrees cooler and will be more moist than baresoil causing differences in crop development.

Performance of a residue spreader on an agricultural harvester may bedeleteriously affected based on a number of different criteria. Forexample, areas with variance in vegetation such as intensity of weeds orcrop plants may have deleterious effects on the residue spreadingoperation. Increased vegetation can increase the mass of residue beingspread by the agricultural harvester.

Or for example, topographic characteristics affect the orientation ofthe agricultural harvester (e.g., pitch and roll) as it travels over theterrain. This orientation of the agricultural harvester affects the wayin which the harvester spreads residue across the field. For instance,when agricultural harvester rolls to either the left or right side, theuphill side can have a shorter residue spread distance.

Or for example, areas with variance in vegetation moisture, such asmoisture in the weed and crop plants, may have deleterious effects onthe residue spreading operation. For instance, material having a highermoisture may spread in a smaller width due to increased friction in theresidue system or due to the increased mass of the material. Or in someinstances, material having a higher moisture may spread farther becauseof increased inertia of the material that resists the effects of airresistance or wind.

A vegetative index map illustratively maps vegetative index values(which may be indicative of vegetative growth) across differentgeographic locations in a field of interest. One example of a vegetativeindex includes a normalized difference vegetation index (NDVI). Thereare many other vegetative indices that are within the scope of thepresent disclosure. In some examples, a vegetative index may be derivedfrom sensor readings of one or more bands of electromagnetic radiationreflected by the plants. Without limitations, these bands may be in themicrowave, infrared, visible, or ultraviolet portions of theelectromagnetic spectrum.

A vegetative index map can be used to identify the presence and locationof vegetation. In some examples, these maps enable weeds to beidentified and georeferenced in the presence of bare soil, crop residue,or other plants, including crop or other weeds. For instance, at the endof a growing season, when a crop is mature, the crop plants may show areduced level of live, growing vegetation. However, weeds often persistin a growing state after the maturity of the crop. Therefore, if avegetative index map is generated relatively late in the growing season,the vegetative index map may be indicative of the location of weeds inthe field.

A topographic map illustratively maps elevations of the ground acrossdifferent geographic locations in a field of interest. Since groundslope is indicative of a change in elevation, having two or moreelevation values allows for calculation of slope across the areas havingknown elevation values. Greater granularity of slope can be accomplishedby having more areas with known elevation values. As an agriculturalharvester travels across the terrain in known directions, the pitch androll of the agricultural harvester can be determined based on the slopeof the ground (i.e., areas of changing elevation). Topographiccharacteristics, when referred to below, can include, but are notlimited to, the elevation, slope (e.g., including the machineorientation relative to the slope), and ground profile (e.g.,roughness).

A vegetation moisture map illustratively maps vegetation moisture acrossdifferent geographic locations in a field of interest. In one example,vegetation moisture can be sensed prior to a harvesting operation by anunmanned aerial vehicle (UAV) equipped with a moisture sensor. As theUAV travels across the field, the vegetation moisture readings aregeolocated to create a vegetation moisture map. This is an example onlyand the vegetation moisture map can be created in other ways as well.For example, the vegetation moisture across a field can be predictedbased on weather conditions, such as precipitation, temperature, orwind, field surface characteristics, such as topography or soilmoisture, or combinations thereof. In some examples, the vegetationmoisture map can be generated by subtracting the difference betweenpotential evapotranspiration and moisture to determine any deficit. Insuch a method, weather characteristics, such as precipitation andtemperature, as well as previously calculated vegetation moistureindexes, can be used as inputs.

The present discussion, thus, proceeds with respect to systems thatreceive an information map predicted or generated based on a prioroperation and also use an in-situ sensor to detect a variable indicativeof one or more of an agricultural characteristic, such as a residuecharacteristic during a harvesting operation. The systems generate amodel that models a relationship between the values on the informationmap and the output values from the in-situ sensor. The model is used togenerate a functional predictive map that predicts, for example, residuecharacteristics at different locations in the field. The functionalpredictive map, generated during the harvesting operation, can bepresented to an operator or other user or used in automaticallycontrolling an agricultural harvester during the harvesting operation orboth. The functional predictive map can be used to control the residuehandling subsystem or other components of an agricultural harvester.

FIG. 1 is a partial pictorial, partial schematic illustration of aself-propelled agricultural harvester 100. In the illustrated example,agricultural harvester 100 is a combine harvester. Further, althoughcombine harvesters are provided as examples throughout the presentdisclosure, it will be appreciated that the present description is alsoapplicable to other types of harvesters, such as cotton harvesters,sugarcane harvesters, self-propelled forage harvesters, windrowers, orother agricultural work machines. Consequently, the present disclosureis intended to encompass the various types of harvesters described andis, thus, not limited to combine harvesters. Moreover, the presentdisclosure is directed to other types of work machines, such asagricultural seeders and sprayers, construction equipment, forestryequipment, and turf management equipment where generation of apredictive map may be applicable. Consequently, the present disclosureis intended to encompass these various types of harvesters and otherwork machines and is, thus, not limited to combine harvesters.

As shown in FIG. 1 , agricultural harvester 100 illustratively includesan operator compartment 101, which can have a variety of differentoperator interface mechanisms for controlling agricultural harvester100. Agricultural harvester 100 includes front-end equipment, such as aheader 102, and a cutter generally indicated at 104. In the illustratedexample, the cutter 104 is included on the header 102. Agriculturalharvester 100 also includes a feeder house 106, a feed accelerator 108,and a thresher generally indicated at 110. The feeder house 106 and thefeed accelerator 108 form part of a material handling subsystem 125.Header 102 is pivotally coupled to a frame 103 of agricultural harvester100 along pivot axis 105. One or more actuators 107 drive movement ofheader 102 about axis 105 in the direction generally indicated by arrow109. Thus, a vertical position of header 102 (the header height) aboveground 111 over which the header 102 travels is controllable byactuating actuator 107. While not shown in FIG. 1 , agriculturalharvester 100 may also include one or more actuators that operate toapply a tilt angle, a roll angle, or both to the header 102 or portionsof header 102. Tilt refers to an angle at which the cutter 104 engagesthe crop. The tilt angle is increased, for example, by controllingheader 102 to point a distal edge 113 of cutter 104 more toward theground. The tilt angle is decreased by controlling header 102 to pointthe distal edge 113 of cutter 104 more away from the ground. The rollangle refers to the orientation of header 102 about the front-to-backlongitudinal axis of agricultural harvester 100.

Thresher 110 illustratively includes a threshing rotor 112 and a set ofconcaves 114. Further, agricultural harvester 100 also includes aseparator 116. Agricultural harvester 100 also includes a cleaningsubsystem or cleaning shoe (collectively referred to as cleaningsubsystem 118) that includes a cleaning fan 120, chaffer 122, and sieve124. The material handling subsystem 125 also includes discharge beater126, tailings elevator 128, clean grain elevator 130, as well asunloading auger 134 and spout 136. The clean grain elevator moves cleangrain into clean grain tank 132. Agricultural harvester 100 alsoincludes a residue subsystem 138 that can include chopper 140 andspreader 142. Agricultural harvester 100 also includes a propulsionsubsystem that includes an engine that drives ground engaging components144, such as wheels or tracks. In some examples, a combine harvesterwithin the scope of the present disclosure may have more than one of anyof the subsystems mentioned above. In some examples, agriculturalharvester 100 may have left and right cleaning subsystems, separators,etc., which are not shown in FIG. 1 .

In operation, and by way of overview, agricultural harvester 100illustratively moves through a field in the direction indicated by arrow147. As agricultural harvester 100 moves, header 102 (and the associatedreel 164) engages the crop to be harvested and gathers the crop towardcutter 104. An operator of agricultural harvester 100 can be a localhuman operator, a remote human operator, or an automated system. Anoperator command is a command by an operator. The operator ofagricultural harvester 100 may determine one or more of a heightsetting, a tilt angle setting, or a roll angle setting for header 102.For example, the operator inputs a setting or settings to a controlsystem, described in more detail below, that controls actuator 107. Thecontrol system may also receive a setting from the operator forestablishing the tilt angle and roll angle of the header 102 andimplement the inputted settings by controlling associated actuators, notshown, that operate to change the tilt angle and roll angle of theheader 102. The actuator 107 maintains header 102 at a height aboveground 111 based on a height setting and, where applicable, at desiredtilt and roll angles. Each of the height, roll, and tilt settings may beimplemented independently of the others. The control system responds toheader error (e.g., the difference between the height setting andmeasured height of header 104 above ground 111 and, in some examples,tilt angle and roll angle errors) with a responsiveness that isdetermined based on a selected sensitivity level. If the sensitivitylevel is set at a greater level of sensitivity, the control systemresponds to smaller header position errors, and attempts to reduce thedetected errors more quickly than when the sensitivity is at a lowerlevel of sensitivity.

Returning to the description of the operation of agricultural harvester100, after crops are cut by cutter 104, the severed crop material ismoved through a conveyor in feeder house 106 toward feed accelerator108, which accelerates the crop material into thresher 110. The cropmaterial is threshed by rotor 112 rotating the crop against concaves114. The threshed crop material is moved by a separator rotor inseparator 116 where a portion of the residue is moved by dischargebeater 126 toward the residue subsystem 138. The portion of residuetransferred to the residue subsystem 138 is chopped by residue chopper140 and spread on the field by spreader 142. In other configurations,the residue is released from the agricultural harvester 100 in awindrow. In other examples, the residue subsystem 138 can include weedseed eliminators (not shown) such as seed baggers or other seedcollectors, or seed crushers or other seed destroyers.

Grain falls to cleaning subsystem 118. Chaffer 122 separates some largerpieces of material from the grain, and sieve 124 separates some of finerpieces of material from the clean grain. Clean grain falls to an augerthat moves the grain to an inlet end of clean grain elevator 130, andthe clean grain elevator 130 moves the clean grain upwards, depositingthe clean grain in clean grain tank 132. Residue is removed from thecleaning subsystem 118 by airflow generated by cleaning fan 120.Cleaning fan 120 directs air along an airflow path upwardly through thesieves and chaffers. The airflow carries residue rearwardly inagricultural harvester 100 toward the residue handling subsystem 138.

Tailings elevator 128 returns tailings to thresher 110 where thetailings are re-threshed. Alternatively, the tailings also may be passedto a separate re-threshing mechanism by a tailings elevator or anothertransport device where the tailings are re-threshed as well.

FIG. 1 also shows that, in one example, agricultural harvester 100includes machine speed sensor 146, one or more separator loss sensors148, a clean grain camera 150, a forward/rearward looking image capturemechanism 151, which may be in the form of a stereo or mono camera, andone or more loss sensors 152 provided in the cleaning subsystem 118.

Machine speed sensor 146 senses the travel speed of agriculturalharvester 100 over the ground. Machine speed sensor 146 may sense thetravel speed of the agricultural harvester 100 by sensing the speed ofrotation of the ground engaging components (such as wheels or tracks), adrive shaft, an axel, or other components. In some instances, the travelspeed may be sensed using a positioning system, such as a globalpositioning system (GPS), a dead reckoning system, a long rangenavigation (LORAN) system, or a wide variety of other systems or sensorsthat provide an indication of travel speed.

Loss sensors 152 illustratively provide an output signal indicative ofthe quantity of grain loss occurring in both the right and left sides ofthe cleaning subsystem 118. In some examples, sensors 152 are strikesensors which count grain strikes per unit of time or per unit ofdistance traveled to provide an indication of the grain loss occurringat the cleaning subsystem 118. The strike sensors for the right and leftsides of the cleaning subsystem 118 may provide individual signals or acombined or aggregated signal. In some examples, sensors 152 may includea single sensor as opposed to separate sensors provided for eachcleaning subsystem 118. Separator loss sensor 148 provides a signalindicative of grain loss in the left and right separators, notseparately shown in FIG. 1 . The separator loss sensors 148 may beassociated with the left and right separators and may provide separategrain loss signals or a combined or aggregate signal. In some instances,sensing grain loss in the separators may also be performed using a widevariety of different types of sensors as well.

Agricultural harvester 100 may also include other sensors andmeasurement mechanisms. For instance, agricultural harvester 100 mayinclude one or more of the following sensors: a header height sensorthat senses a height of header 102 above ground 111; stability sensorsthat sense oscillation or bouncing motion (and amplitude) ofagricultural harvester 100; a residue setting sensor that is configuredto sense whether agricultural harvester 100 is configured to chop theresidue, produce a windrow, etc.; a cleaning shoe fan speed sensor tosense the speed of fan 120; a concave clearance sensor that sensesclearance between the rotor 112 and concaves 114; a threshing rotorspeed sensor that senses a rotor speed of rotor 112; a chaffer clearancesensor that senses the size of openings in chaffer 122; a sieveclearance sensor that senses the size of openings in sieve 124; amaterial other than grain (MOG) moisture sensor that senses a moisturelevel of the MOG passing through agricultural harvester 100; one or moremachine setting sensors configured to sense various configurablesettings of agricultural harvester 100; a machine orientation sensorthat senses the orientation of agricultural harvester 100; and cropproperty sensors that sense a variety of different types of cropproperties, such as crop type, crop moisture, and other crop properties.Crop property sensors may also be configured to sense characteristics ofthe severed crop material as the crop material is being processed byagricultural harvester 100. For example, in some instances, the cropproperty sensors may sense grain quality such as broken grain, MOGlevels; grain constituents such as starches and protein; and grain feedrate as the grain travels through the feeder house 106, clean grainelevator 130, or elsewhere in the agricultural harvester 100. The cropproperty sensors may also sense the feed rate of biomass through feederhouse 106, through the separator 116 or elsewhere in agriculturalharvester 100. The crop property sensors may also sense the feed rate asa mass flow rate of grain through elevator 130 or through other portionsof the agricultural harvester 100 or provide other output signalsindicative of other sensed variables.

Prior to describing how agricultural harvester 100 generates afunctional predictive residue map, and uses the functional predictiveresidue map for control, a brief description of some of the items onagricultural harvester 100, and their operation, will first bedescribed. The description of FIGS. 2 and 3 describe receiving a generaltype of information map and combining information from the informationmap with a georeferenced sensor signal generated by an in-situ sensor,where the sensor signal is indicative of a characteristic in the field,such as characteristics of crop or weeds present in the field.Characteristics of the field may include, but are not limited to,characteristics of a field such as slope, weed intensity, weed type,soil moisture, surface quality; characteristics of crop properties suchas crop height, crop moisture, crop density, crop state; characteristicsof grain properties such as grain moisture, grain size, grain testweight; and characteristics of machine performance such as loss levels,job quality, fuel consumption, and power utilization. A relationshipbetween the characteristic values obtained from in-situ sensor signalsand the information map values is identified, and that relationship isused to generate a new functional predictive map. A functionalpredictive map predicts values at different geographic locations in afield, and one or more of those values may be used for controlling amachine, such as one or more subsystems of an agricultural harvester. Insome instances, a functional predictive map can be presented to a user,such as an operator of an agricultural work machine, which may be anagricultural harvester. A functional predictive map may be presented toa user visually, such as via a display, haptically, or audibly. The usermay interact with the functional predictive map to perform editingoperations and other user interface operations. In some instances, afunctional predictive map can be used for one or more of controlling anagricultural work machine, such as an agricultural harvester,presentation to an operator or other user, and presentation to anoperator or user for interaction by the operator or user.

After the general approach is described with respect to FIGS. 2 and 3 ,a more specific approach for generating a functional predictive residuemap that can be presented to an operator or user, or used to controlagricultural harvester 100, or both is described with respect to FIGS. 4and 5 . Again, while the present discussion proceeds with respect to theagricultural harvester and, particularly, a combine harvester, the scopeof the present disclosure encompasses other types of agriculturalharvesters or other agricultural work machines.

FIG. 2 is a block diagram showing some portions of an exampleagricultural harvester 100. FIG. 2 shows that agricultural harvester 100illustratively includes one or more processors or servers 201, datastore 202, geographic position sensor 204, communication system 206, andone or more in-situ sensors 208 that sense one or more agriculturalcharacteristics of a field concurrent with a harvesting operation. Anagricultural characteristic can include any characteristic that can havean effect of the harvesting operation. Some examples of agriculturalcharacteristics include characteristics of the harvesting machine, thefield, the plants on the field, and the weather. Other types ofagricultural characteristics are also included. The in-situ sensors 208generate values corresponding to the sensed characteristics. Theagricultural harvester 100 also includes a predictive model orrelationship generator (collectively referred to hereinafter as“predictive model generator 210”), predictive map generator 212, controlzone generator 213, control system 214, one or more controllablesubsystems 216, and an operator interface mechanism 218. Theagricultural harvester 100 can also include a wide variety of otheragricultural harvester functionality 220. The in-situ sensors 208include, for example, on-board sensors 222, remote sensors 224, andother sensors 226 that sense characteristics of a field during thecourse of an agricultural operation. Predictive model generator 210illustratively includes an information variable-to-in-situ variablemodel generator 228, and predictive model generator 210 can includeother items 230. Control system 214 includes communication systemcontroller 229, operator interface controller 231, a settings controller232, path planning controller 234, feed rate controller 236, header andreel controller 238, draper belt controller 240, deck plate positioncontroller 242, residue system controller 244, machine cleaningcontroller 245, zone controller 247, and system 214 can include otheritems 246. Controllable subsystems 216 include machine and headeractuators 248, propulsion subsystem 250, steering subsystem 252, residuesubsystem 138, machine cleaning subsystem 254, and subsystems 216 caninclude a wide variety of other subsystems 256.

FIG. 2 also shows that agricultural harvester 100 can receiveinformation map 258. As described below, the information map 258includes, for example, a vegetative index map or a vegetation map from aprior operation or a predictive residue map. However, information map258 may also encompass other types of data that were obtained prior to aharvesting operation or a map from a prior operation. FIG. 2 also showsthat an operator 260 may operate the agricultural harvester 100. Theoperator 260 interacts with operator interface mechanisms 218. In someexamples, operator interface mechanisms 218 may include joysticks,levers, a steering wheel, linkages, pedals, buttons, dials, keypads,user actuatable elements (such as icons, buttons, etc.) on a userinterface display device, a microphone and speaker (where speechrecognition and speech synthesis are provided), among a wide variety ofother types of control devices. Where a touch sensitive display systemis provided, operator 260 may interact with operator interfacemechanisms 218 using touch gestures. These examples described above areprovided as illustrative examples and are not intended to limit thescope of the present disclosure. Consequently, other types of operatorinterface mechanisms 218 may be used and are within the scope of thepresent disclosure.

Information map 258 may be downloaded onto agricultural harvester 100and stored in data store 202, using communication system 206 or in otherways. In some examples, communication system 206 may be a cellularcommunication system, a system for communicating over a wide areanetwork or a local area network, a system for communicating over a nearfield communication network, or a communication system configured tocommunicate over any of a variety of other networks or combinations ofnetworks. Communication system 206 may also include a system thatfacilitates downloads or transfers of information to and from a securedigital (SD) card or a universal serial bus (USB) card or both.

Geographic position sensor 204 illustratively senses or detects thegeographic position or location of agricultural harvester 100.Geographic position sensor 204 can include, but is not limited to, aglobal navigation satellite system (GNSS) receiver that receives signalsfrom a GNSS satellite transmitter. Geographic position sensor 204 canalso include a real-time kinematic (RTK) component that is configured toenhance the precision of position data derived from the GNSS signal.Geographic position sensor 204 can include a dead reckoning system, acellular triangulation system, or any of a variety of other geographicposition sensors.

In-situ sensors 208 may be any of the sensors described above withrespect to FIG. 1 . In-situ sensors 208 include on-board sensors 222that are mounted on-board agricultural harvester 100. Such sensors mayinclude, for instance, a perception sensor (e.g., a rearward lookingmono or stereo camera system and image processing system), image sensorsthat are internal to agricultural harvester 100 (such as the clean graincamera or cameras mounted to identify material that is exitingagricultural harvester 100 through the residue subsystem or from thecleaning subsystem). The in-situ sensors 208 also include remote in-situsensors 224 that capture in-situ information. In-situ data include datataken from a sensor on-board the harvester or taken by any sensor wherethe data are detected during the harvesting operation.

Predictive model generator 210 generates a model that is indicative of arelationship between the values sensed by the in-situ sensor 208 and ametric mapped to the field by the information map 258. For example, ifthe information map 258 maps a vegetative index value to differentlocations in the field, and the in-situ sensor 208 is sensing a valueindicative of residue spread width, then information variable-to-in-situvariable model generator 228 generates a predictive residue model thatmodels the relationship between vegetative index and residue spreadwidth. The predictive residue model can also be generated based onvalues from the information map 258 and multiple in-situ data valuesgenerated by in-situ sensors 208. Then, predictive map generator 212uses the predictive residue model generated by predictive modelgenerator 210 to generate a functional predictive residue map thatpredicts the value of multiple values sensed by the multiple in-situsensors 208 at different locations in the field based upon theinformation map 258. In some examples, the type of values in thefunctional predictive map 263 may be the same as the in-situ data typesensed by the in-situ sensors 208. In some instances, the type of valuesin the functional predictive map 263 may have different units from thedata sensed by the in-situ sensors 208. In some examples, the type ofvalues in the functional predictive map 263 may be different from thedata type sensed by the in-situ sensors 208 but have a relationship tothe type of data type sensed by the in-situ sensors 208. For example, insome examples, the data type sensed by the in-situ sensors 208 may beindicative of the type of values in the functional predictive map 263.In some examples, the type of data in the functional predictive map 263may be different than the data type in the information map 258. In someinstances, the type of data in the functional predictive map 263 mayhave different units from the data in the information map 258. In someexamples, the type of data in the functional predictive map 263 may bedifferent from the data type in the information map 258 but has arelationship to the data type in the information map 258. For example,in some examples, the data type in the information map 258 may beindicative of the type of data in the functional predictive map 263. Insome examples, the type of data in the functional predictive map 263 isdifferent than one of, or both of the in-situ data type sensed by thein-situ sensors 208 and the data type in the information map 258. Insome examples, the type of data in the functional predictive map 263 isthe same as one of, or both of, of the in-situ data type sensed by thein-situ sensors 208 and the data type in information map 258. In someexamples, the type of data in the functional predictive map 263 is thesame as one of the in-situ data type sensed by the in-situ sensors 208or the data type in the information map 258, and different than theother.

As shown in FIG. 2 , predictive map 264 predicts the value of a sensedcharacteristic (sensed by in-situ sensors 208), or a characteristicrelated to the sensed characteristic, at various locations across thefield based upon an information value in information map 258 at thoselocations and using the predictive model. For example, if predictivemodel generator 210 has generated a predictive model indicative of arelationship between a vegetation moisture and residue spread widththen, given the moisture value at different locations across the field,predictive map generator 212 generates a predictive map 264 thatpredicts the value of the residue spread width at different locationsacross the field. The moisture value, obtained from the moisture map, atthose locations and the relationship between moisture value and residuespread width, obtained from the predictive model, are used to generatethe predictive map 264.

Some variations in the data types that are mapped in the information map258, the data types sensed by in-situ sensors 208, and the data typespredicted on the predictive map 264 will now be described.

In some examples, the data type in the information map 258 is differentfrom the data type sensed by in-situ sensors 208, yet the data type inthe predictive map 264 is the same as the data type sensed by thein-situ sensors 208. For instance, the information map 258 may be avegetative index map, and the variable sensed by the in-situ sensors 208may be yield. The predictive map 264 may then be a predictive yield mapthat maps predicted yield values to different geographic locations inthe field. In another example, the information map 258 may be avegetative index map, and the variable sensed by the in-situ sensors 208may be crop height. The predictive map 264 may then be a predictive cropheight map that maps predicted crop height values to differentgeographic locations in the field.

Also, in some examples, the data type in the information map 258 isdifferent from the data type sensed by in-situ sensors 208, and the datatype in the predictive map 264 is different from both the data type inthe information map 258 and the data type sensed by the in-situ sensors208. For instance, the information map 258 may be a vegetative indexmap, and the variable sensed by the in-situ sensors 208 may be cropheight. The predictive map 264 may then be a predictive biomass map thatmaps predicted biomass values to different geographic locations in thefield. In another example, the information map 258 may be a vegetativeindex map, and the variable sensed by the in-situ sensors 208 may beyield. The predictive map 264 may then be a predictive speed map thatmaps predicted harvester speed values to different geographic locationsin the field.

In some examples, the information map 258 is from a prior pass throughthe field during a prior operation and the data type is different fromthe data type sensed by in-situ sensors 208, yet the data type in thepredictive map 264 is the same as the data type sensed by the in-situsensors 208. For instance, the information map 258 may be a seedpopulation map generated during planting, and the variable sensed by thein-situ sensors 208 may be stalk size. The predictive map 264 may thenbe a predictive stalk size map that maps predicted stalk size values todifferent geographic locations in the field. In another example, theinformation map 258 may be a seeding hybrid map, and the variable sensedby the in-situ sensors 208 may be crop state such as standing crop ordown crop. The predictive map 264 may then be a predictive crop statemap that maps predicted crop state values to different geographiclocations in the field.

In some examples, the information map 258 is from a prior pass throughthe field during a prior operation and the data type is the same as thedata type sensed by in-situ sensors 208, and the data type in thepredictive map 264 is also the same as the data type sensed by thein-situ sensors 208. For instance, the information map 258 may be ayield map generated during a previous year, and the variable sensed bythe in-situ sensors 208 may be yield. The predictive map 264 may then bea predictive yield map that maps predicted yield values to differentgeographic locations in the field. In such an example, the relativeyield differences in the georeferenced information map 258 from theprior year can be used by predictive model generator 210 to generate apredictive model that models a relationship between the relative yielddifferences on the information map 258 and the yield values sensed byin-situ sensors 208 during the current harvesting operation. Thepredictive model is then used by predictive map generator 210 togenerate a predictive yield map.

In some examples, predictive map 264 can be provided to the control zonegenerator 213. Control zone generator 213 groups adjacent portions of anarea into one or more control zones based on data values of predictivemap 264 that are associated with those adjacent portions. A control zonemay include two or more contiguous portions of an area, such as a field,for which a control parameter corresponding to the control zone forcontrolling a controllable subsystem is constant. For example, aresponse time to alter a setting of controllable subsystems 216 may beinadequate to satisfactorily respond to changes in values contained in amap, such as predictive map 264. In that case, control zone generator213 parses the map and identifies control zones that are of a definedsize to accommodate the response time of the controllable subsystems216. In another example, control zones may be sized to reduce wear fromexcessive actuator movement resulting from continuous adjustment. Insome examples, there may be a different set of control zones for eachcontrollable subsystem 216 or for groups of controllable subsystems 216.The control zones may be added to the predictive map 264 to obtainpredictive control zone map 265. Predictive control zone map 265 canthus be similar to predictive map 264 except that predictive controlzone map 265 includes control zone information defining the controlzones. Thus, a functional predictive map 263, as described herein, mayor may not include control zones. Both predictive map 264 and predictivecontrol zone map 265 are functional predictive maps 263. In one example,a functional predictive map 263 does not include control zones, such aspredictive map 264. In another example, a functional predictive map 263does include control zones, such as predictive control zone map 265. Insome examples, multiple crops may be simultaneously present in a fieldif an intercrop production system is implemented. In that case,predictive map generator 212 and control zone generator 213 are able toidentify the location and characteristics of the two or more crops andthen generate predictive map 264 and predictive control zone map 265accordingly.

It will also be appreciated that control zone generator 213 can clustervalues to generate control zones and the control zones can be added topredictive control zone map 265, or a separate map, showing only thecontrol zones that are generated. In some examples, the control zonesmay be used for controlling or calibrating agricultural harvester 100 orboth. In other examples, the control zones may be presented to theoperator 260 and used to control or calibrate agricultural harvester100, and, in other examples, the control zones may be presented to theoperator 260 or another user or stored for later use.

Predictive map 264 or predictive control zone map 265 or both areprovided to control system 214, which generates control signals basedupon the predictive map 264 or predictive control zone map 265 or both.In some examples, communication system controller 229 controlscommunication system 206 to communicate the predictive map 264 orpredictive control zone map 265 or control signals based on thepredictive map 264 or predictive control zone map 265 to otheragricultural harvesters that are harvesting in the same field. In someexamples, communication system controller 229 controls the communicationsystem 206 to send the predictive map 264, predictive control zone map265, or both to other remote systems.

Operator interface controller 231 is operable to generate controlsignals to control operator interface mechanisms 218. The operatorinterface controller 231 is also operable to present the predictive map264 or predictive control zone map 265 or other information derived fromor based on the predictive map 264, predictive control zone map 265, orboth to operator 260. Operator 260 may be a local operator or a remoteoperator. As an example, controller 231 generates control signals tocontrol a display mechanism to display one or both of predictive map 264and predictive control zone map 265 for the operator 260. Controller 231may generate operator actuatable mechanisms that are displayed and canbe actuated by the operator to interact with the displayed map. Theoperator can edit the map by, for example, correcting a residue spreaddisplayed on the map, based on the operator's observation. Settingscontroller 232 can generate control signals to control various settingson the agricultural harvester 100 based upon predictive map 264, thepredictive control zone map 265, or both. For instance, settingscontroller 232 can generate control signals to control machine andheader actuators 248. In response to the generated control signals, themachine and header actuators 248 operate to control, for example, one ormore of the sieve and chaffer settings, concave clearance, rotorsettings, cleaning fan speed settings, header height, headerfunctionality, reel speed, reel position, draper functionality (whereagricultural harvester 100 is coupled to a draper header), corn headerfunctionality, internal distribution control and other actuators 248that affect the other functions of the agricultural harvester 100. Pathplanning controller 234 illustratively generates control signals tocontrol steering subsystem 252 to steer agricultural harvester 100according to a desired path. Path planning controller 234 can control apath planning system to generate a route for agricultural harvester 100and can control propulsion subsystem 250 and steering subsystem 252 tosteer agricultural harvester 100 along that route. Feed rate controller236 can control various subsystems, such as propulsion subsystem 250 andmachine actuators 248, to control a feed rate based upon the predictivemap 264 or predictive control zone map 265 or both. For instance, asagricultural harvester 100 approaches a weed patch having an intensityvalue above a selected threshold, feed rate controller 236 may reducethe speed of machine 100 to maintain constant feed rate of biomassthrough the machine. Header and reel controller 238 can generate controlsignals to control a header or a reel or other header functionality.Draper belt controller 240 can generate control signals to control adraper belt or other draper functionality based upon the predictive map264, predictive control zone map 265, or both. Deck plate positioncontroller 242 can generate control signals to control a position of adeck plate included on a header based on predictive map 264 orpredictive control zone map 265 or both. Residue system controller 244can generate control signals to control a residue subsystem 138 basedupon predictive map 264 or predictive control zone map 265, or both.Machine cleaning controller 245 can generate control signals to controlmachine cleaning subsystem 254. Other controllers included on theagricultural harvester 100 can control other subsystems based on thepredictive map 264 or predictive control zone map 265 or both as well.

FIGS. 3A and 3B (collectively referred to herein as FIG. 3 ) show a flowdiagram illustrating one example of the operation of agriculturalharvester 100 in generating a predictive map 264 and predictive controlzone map 265 based upon information map 258.

At 280, agricultural harvester 100 receives information map 258.Examples of information map 258 or receiving information map 258 arediscussed with respect to blocks 281, 282, 284 and 286. As discussedabove, information map 258 maps values of a variable, corresponding to afirst characteristic, to different locations in the field, as indicatedat block 282. As indicated at block 281, receiving the information map258 may involve selecting one or more of a plurality of possibleinformation maps that are available. For instance, one information mapmay be a vegetative index map generated from aerial imagery. Anotherinformation map may be a map generated during a prior pass through thefield which may have been performed by a different machine performing aprevious operation in the field, such as a sprayer or other machine. Theprocess by which one or more information maps are selected can bemanual, semi-automated, or automated. The information map 258 is basedon data collected prior to a current harvesting operation. This isindicated by block 284. For instance, the data may be collected based onaerial images taken during a previous year, or earlier in the currentgrowing season, or at other times. The data may be based on datadetected in ways other than using aerial images. For instance,agricultural harvester 100 may be fitted with a sensor, such as aninternal optical sensor, that identifies weed seeds that are exitingagricultural harvester 100. The weed seed data detected by the sensorduring a previous year's harvest may be used as data used to generatethe information map 258. The sensed weed data may be combined with otherdata to generate the information map 258. For example, based upon amagnitude of the weed seeds exiting agricultural harvester 100 atdifferent locations and based upon other factors, such as whether theseeds are being spread by a spreader or dropped in a windrow; theweather conditions, such as wind, when the seeds are being dropped orspread; drainage conditions which may move seeds around in the field; orother information, the location of those weed seeds can be predicted sothat the information map 258 maps the predicted seed locations in thefield. The data for the information map 258 can be transmitted toagricultural harvester 100 using communication system 206 and stored indata store 202. The data for the information map 258 can be provided toagricultural harvester 100 using communication system 206 in other waysas well, and this is indicated by block 286 in the flow diagram of FIG.3 . In some examples, the information map 258 can be received bycommunication system 206.

Upon commencement of a harvesting operation, in-situ sensors 208generate sensor signals indicative of one or more in-situ data valuesindicative of a characteristic, for example, a residue characteristic,as indicated by block 288. Examples of in-situ sensors are discussedwith respect to blocks 222, 290, and 226. As explained above, thein-situ sensors 208 include on-board sensors 222, such as a rearwardfacing camera; remote in-situ sensors 224, such as UAV-based sensorsflown at a time to gather in-situ data, shown in block 290; or othertypes of in-situ sensors, designated by in-situ sensors 226. In someexamples, data from on-board sensors is georeferenced using position,heading, or speed data from geographic position sensor 204.

Predictive model generator 210 controls the informationvariable-to-in-situ variable model generator 228 to generate a modelthat models a relationship between the mapped values contained in theinformation map 258 and the in-situ values sensed by the in-situ sensors208 as indicated by block 292. The characteristics or data typesrepresented by the mapped values in the information map 258 and thein-situ values sensed by the in-situ sensors 208 may be the samecharacteristics or data type or different characteristics or data types.

The relationship or model generated by predictive model generator 210 isprovided to predictive map generator 212. Predictive map generator 212generates a predictive map 264 that predicts a value of thecharacteristic sensed by the in-situ sensors 208 at different geographiclocations in a field being harvested, or a different characteristic thatis related to the characteristic sensed by the in-situ sensors 208,using the predictive model and the information map 258, as indicated byblock 294.

It should be noted that, in some examples, the information map 258 mayinclude two or more different maps or two or more different map layersof a single map. Each map layer may represent a different data type fromthe data type of another map layer or the map layers may have the samedata type that were obtained at different times. Each map in the two ormore different maps or each layer in the two or more different maplayers of a map maps a different type of variable to the geographiclocations in the field. In such an example, predictive model generator210 generates a predictive model that models the relationship betweenthe in-situ data and each of the different variables mapped by the twoor more different maps or the two or more different map layers.Similarly, the in-situ sensors 208 can include two or more sensors eachsensing a different type of variable. Thus, the predictive modelgenerator 210 generates a predictive model that models the relationshipsbetween each type of variable mapped by the information map 258 and eachtype of variable sensed by the in-situ sensors 208. Predictive mapgenerator 212 can generate a functional predictive map 263 that predictsa value for each sensed characteristic sensed by the in-situ sensors 208(or a characteristic related to the sensed characteristic) at differentlocations in the field being harvested using the predictive model andeach of the maps or map layers in the information map 258.

Predictive map generator 212 configures the predictive map 264 so thatthe predictive map 264 is actionable (or consumable) by control system214. Predictive map generator 212 can provide the predictive map 264 tothe control system 214 or to control zone generator 213 or both. Someexamples of different ways in which the predictive map 264 can beconfigured or output are described with respect to blocks 296, 295, 299,and 297. For instance, predictive map generator 212 configurespredictive map 264 so that predictive map 264 includes values that canbe read by control system 214 and used as the basis for generatingcontrol signals for one or more of the different controllable subsystemsof the agricultural harvester 100, as indicated by block 296.

Control zone generator 213 can divide the predictive map 264 intocontrol zones based on the values on the predictive map 264.Contiguously-geolocated values that are within a threshold value of oneanother can be grouped into a control zone. The threshold value can be adefault threshold value, or the threshold value can be set based on anoperator input, based on an input from an automated system, or based onother criteria. A size of the zones may be based on a responsiveness ofthe control system 214, the controllable subsystems 216, based on wearconsiderations, or on other criteria as indicated by block 295.Predictive map generator 212 configures predictive map 264 forpresentation to an operator or other user. Control zone generator 213can configure predictive control zone map 265 for presentation to anoperator or other user. This is indicated by block 299. When presentedto an operator or other user, the presentation of the predictive map 264or predictive control zone map 265 or both may contain one or more ofthe predictive values on the predictive map 264 correlated to geographiclocation, the control zones on predictive control zone map 265correlated to geographic location, and settings values or controlparameters that are used based on the predicted values on map 264 orzones on predictive control zone map 265. The presentation can, inanother example, include more abstracted information or more detailedinformation. The presentation can also include a confidence level thatindicates an accuracy with which the predictive values on predictive map264 or the zones on predictive control zone map 265 conform to measuredvalues that may be measured by sensors on agricultural harvester 100 asagricultural harvester 100 moves through the field. Further whereinformation is presented to more than one location, an authenticationand authorization system can be provided to implement authentication andauthorization processes. For instance, there may be a hierarchy ofindividuals that are authorized to view and change maps and otherpresented information. By way of example, an on-board display device mayshow the maps in near real time locally on the machine, or the maps mayalso be generated at one or more remote locations, or both. In someexamples, each physical display device at each location may beassociated with a person or a user permission level. The user permissionlevel may be used to determine which display markers are visible on thephysical display device and which values the corresponding person maychange. As an example, a local operator of machine 100 may be unable tosee the information corresponding to the predictive map 264 or make anychanges to machine operation. A supervisor, such as a supervisor at aremote location, however, may be able to see the predictive map 264 onthe display but be prevented from making any changes. A manager, who maybe at a separate remote location, may be able to see all of the elementson predictive map 264 and also be able to change the predictive map 264.In some instances, the predictive map 264 is accessible and changeableby a manager located remotely may be used in machine control. This isone example of an authorization hierarchy that may be implemented. Thepredictive map 264 or predictive control zone map 265 or both can beconfigured in other ways as well, as indicated by block 297.

At block 298, input from geographic position sensor 204 and otherin-situ sensors 208 are received by the control system. Particularly, atblock 300, control system 214 detects an input from the geographicposition sensor 204 identifying a geographic location of agriculturalharvester 100. Block 302 represents receipt by the control system 214 ofsensor inputs indicative of trajectory or heading of agriculturalharvester 100, and block 304 represents receipt by the control system214 of a speed of agricultural harvester 100. Block 306 representsreceipt by the control system 214 of other information from variousin-situ sensors 208.

At block 308, control system 214 generates control signals to controlthe controllable subsystems 216 based on the predictive map 264 orpredictive control zone map 265 or both and the input from thegeographic position sensor 204 and any other in-situ sensors 208. Atblock 310, control system 214 applies the control signals to thecontrollable subsystems. It will be appreciated that the particularcontrol signals that are generated, and the particular controllablesubsystems 216 that are controlled, may vary based upon one or moredifferent things. For example, the control signals that are generatedand the controllable subsystems 216 that are controlled may be based onthe type of predictive map 264 or predictive control zone map 265 orboth that is being used. Similarly, the control signals that aregenerated, the controllable subsystems 216 that are controlled, and thetiming of the control signals can be based on various latencies of cropflow through the agricultural harvester 100 and the responsiveness ofthe controllable subsystems 216.

By way of example, a generated predictive map 264 in the form of apredictive residue map can be used to control one or more subsystems216. For instance, the predictive residue map can include residuecharacteristic values georeferenced to locations within the field beingharvested. The residue characteristic values from the predictive residuemap can be extracted and used to control one or more components ofresidue system 138. For instance, the spreader 142 can be controlled tospread residue more uniformly or in desired locations. Consequently, awide variety of other control signals can be generated using valuesobtained from a predictive residue map or other type of predictive mapto control one or more of the controllable subsystems 216.

At block 312, a determination is made as to whether the harvestingoperation has been completed. If harvesting is not completed, theprocessing advances to block 314 where in-situ sensor data fromgeographic position sensor 204 and in-situ sensors 208 (and perhapsother sensors) continue to be read.

In some examples, at block 316, agricultural harvester 100 can alsodetect learning trigger criteria to perform machine learning on one ormore of the predictive map 264, predictive control zone map 265, themodel generated by predictive model generator 210, the zones generatedby control zone generator 213, one or more control algorithmsimplemented by the controllers in the control system 214, and othertriggered learning.

The learning trigger criteria can include any of a wide variety ofdifferent criteria. Some examples of detecting trigger criteria arediscussed with respect to blocks 318, 320, 321, 322, and 324. Forinstance, in some examples, triggered learning can involve recreation ofa relationship used to generate a predictive model when a thresholdamount of in-situ sensor data are obtained from in-situ sensors 208. Insuch examples, receipt of an amount of in-situ sensor data from thein-situ sensors 208 that exceeds a threshold triggers or causes thepredictive model generator 210 to generate a new predictive model thatis used by predictive map generator 212. Thus, as agricultural harvester100 continues a harvesting operation, receipt of the threshold amount ofin-situ sensor data from the in-situ sensors 208 triggers the creationof a new relationship represented by a predictive model generated bypredictive model generator 210. Further, new predictive map 264,predictive control zone map 265, or both can be regenerated using thenew predictive model. Block 318 represents detecting a threshold amountof in-situ sensor data used to trigger creation of a new predictivemodel.

In other examples, the learning trigger criteria may be based on howmuch the in-situ sensor data from the in-situ sensors 208 are changing,such as over time or compared to previous values. For example, ifvariations within the in-situ sensor data (or the relationship betweenthe in-situ sensor data and the information in information map 258) arewithin a selected range or is less than a defined amount, or below athreshold value, then a new predictive model is not generated by thepredictive model generator 210. As a result, the predictive mapgenerator 212 does not generate a new predictive map 264, predictivecontrol zone map 265, or both. However, if variations within the in-situsensor data are outside of the selected range, are greater than thedefined amount, or are above the threshold value, for example, then thepredictive model generator 210 generates a new predictive model usingall or a portion of the newly received in-situ sensor data that thepredictive map generator 212 uses to generate a new predictive map 264.At block 320, variations in the in-situ sensor data, such as a magnitudeof an amount by which the data exceeds the selected range or a magnitudeof the variation of the relationship between the in-situ sensor data andthe information in the information map 258, can be used as a trigger tocause generation of a new predictive model and predictive map. Keepingwith the examples described above, the threshold, the range, and thedefined amount can be set to default values; set by an operator or userinteraction through a user interface; set by an automated system; or setin other ways.

Other learning trigger criteria can also be used. For instance, ifpredictive model generator 210 switches to a different information map(different from the originally selected information map 258), thenswitching to the different information map may trigger re-learning bypredictive model generator 210, predictive map generator 212, controlzone generator 213, control system 214, or other items. In anotherexample, transitioning of agricultural harvester 100 to a differenttopography or to a different control zone may be used as learningtrigger criteria as well.

In some instances, operator 260 can also edit the predictive map 264 orpredictive control zone map 265 or both. The edits can change a value onthe predictive map 264; change a size, shape, position, or existence ofa control zone on predictive control zone map 265; or both. Block 321shows that edited information can be used as learning trigger criteria.

In some instances, it may also be that operator 260 observes thatautomated control of a controllable subsystem, is not what the operatordesires. In such instances, the operator 260 may provide a manualadjustment to the controllable subsystem reflecting that the operator260 desires the controllable subsystem to operate in a different waythan is being commanded by control system 214. Thus, manual alterationof a setting by the operator 260 can cause one or more of predictivemodel generator 210 to relearn a model, predictive map generator 212 toregenerate map 264, control zone generator 213 to regenerate one or morecontrol zones on predictive control zone map 265, and control system 214to relearn a control algorithm or to perform machine learning on one ormore of the controller components 232 through 246 in control system 214based upon the adjustment by the operator 260, as shown in block 322.Block 324 represents the use of other triggered learning criteria.

In other examples, relearning may be performed periodically orintermittently based, for example, upon a selected time interval such asa discrete time interval or a variable time interval, as indicated byblock 326.

If relearning is triggered, whether based upon learning trigger criteriaor based upon passage of a time interval, as indicated by block 326,then one or more of the predictive model generator 210, predictive mapgenerator 212, control zone generator 213, and control system 214performs machine learning to generate a new predictive model, a newpredictive map, a new control zone, and a new control algorithm,respectively, based upon the learning trigger criteria. The newpredictive model, the new predictive map, and the new control algorithmare generated using any additional data that has been collected sincethe last learning operation was performed. Performing relearning isindicated by block 328.

If the harvesting operation has been completed, operation moves fromblock 312 to block 330 where one or more of the predictive map 264,predictive control zone map 265, and predictive model generated bypredictive model generator 210 are stored. The predictive map 264,predictive control zone map 265, and predictive model may be storedlocally on data store 202 or sent to a remote system using communicationsystem 206 for later use.

It will be noted that while some examples herein describe predictivemodel generator 210 and predictive map generator 212 receiving aninformation map in generating a predictive model and a functionalpredictive map, respectively, in other examples, the predictive modelgenerator 210 and predictive map generator 212 can receive, ingenerating a predictive model and a functional predictive map,respectively other types of maps, including predictive maps, such as afunctional predictive map generated during the harvesting operation.

FIG. 4 is a block diagram of a portion of the agricultural harvester 100shown in FIG. 1 . Particularly, FIG. 4 shows, among other things,examples of the predictive model generator 210 and the predictive mapgenerator 212 in more detail. FIG. 4 also illustrates information flowamong the various components shown. The predictive model generator 210receives one or more of a vegetative index map 331, a moisture map 332and a topographic map 333 as an information map. Predictive modelgenerator 210 also receives a geographic location 334, or an indicationof geographic location, from geographic position sensor 204. In-situsensors 208 illustratively include a residue sensor, such as residuesensor 336, as well as a processing system 338. In some instances,residue sensor 336 may be located on board the agricultural harvester100. In other examples, residue sensor 336 is remote from agriculturalharvester 100 and senses an area that agricultural harvester 100 hastravelled over and senses the residue characteristic. The processingsystem 338 processes sensor data generated from residue sensor 336 togenerate processed data, some examples of which are described below.

In some examples, residue sensor 336 may be an optical sensor, such as acamera, that generates images of an area of a field that has beenharvested. In some instances, the optical sensor may be arranged on theagricultural harvester 100 to collect images of an area adjacent to theagricultural harvester 100, such as in an area that lies rearwardly of,to the side of, or in another direction relative to the agriculturalharvester 100 as agricultural harvester 100 moves through the fieldduring a harvesting operation. The optical sensor may also be located onor inside of the agricultural harvester 100 to obtain images of one ormore portions of the exterior or interior of the agricultural harvester100. Processing system 338 processes one or more images obtained via theresidue sensor 336 to generate processed image data identifying one ormore characteristics of residue in the image. Residue characteristicsdetected by the processing system 338 may include a dimensional spread(width and rearward distance of the spread), residue uniformity, andresidue content (e.g., chopped straw quality, straw size, weed seeds,etc.).

In-situ sensor 208 may be or include other types of sensors, such as acamera located along a path by which severed crop material travels inagricultural harvester 100 (referred to hereinafter as “processcamera”). A process camera may be located internal to the agriculturalharvester 100 and may capture images of crop material, including seeds,as the crop material moves through or is expelled from the agriculturalharvester 100. Thus, in some examples, the processing system 338 isoperable to detect the presence of material passing through theagricultural harvester 100 during the course of a harvesting operation.

In other examples, residue sensor 336 can rely on wavelength(s) ofelectromagnetic energy and the way the electromagnetic energy isreflected by, absorbed by, attenuated by, or transmitted through residuematerial. The residue sensor 336 may sense other electromagneticproperties of residue material, such as electrical permittivity, whenthe residue material passes between two capacitive plates. Othermaterial properties and sensors may also be used. In some examples, rawor processed data from residue sensor 336 may be presented to operator260 via operator interface mechanism 218. Operator 260 may be onboardthe agricultural harvester 100 or at a remote location.

The present discussion proceeds with respect to an example in whichresidue sensor 336 is an image sensor, such as a camera. It will beappreciated that this is just one example, and the sensors mentionedabove, as other examples of residue sensor 336, are contemplated hereinas well. As shown in FIG. 4 , the example predictive model generator 210includes one or more of a residue characteristic-to-vegetative indexmodel generator 342, a residue characteristic-to-moisture modelgenerator 344, and a residue characteristic-to-topographiccharacteristic model generator 346. In other examples, the predictivemodel generator 210 may include additional, fewer, or differentcomponents than those shown in the example of FIG. 4 . Consequently, insome examples, the predictive model generator 210 may include otheritems 348 as well, which may include other types of predictive modelgenerators to generate other types of residue models.

Model generator 342 identifies a relationship between residuecharacteristic detected in image data 340, at a geographic locationcorresponding to where the image data 340 was georeferenced, andvegetative index values from the vegetative index map 331 correspondingto the same location in the field where the residue characteristic wasdetected. Based on this relationship established by model generator 342,model generator 342 generates a predictive residue model 350. Thepredictive residue model 350 is used by residue map generator 352 topredict residue characteristics at different locations in the fieldbased upon the georeferenced vegetative index value contained in thevegetative index map 331 at the same locations in the field.

Model generator 344 identifies a relationship between the residuecharacteristic in the processed image data 340, at a geographic locationcorresponding to where the image data 340 was georeferenced, and themoisture value at the same geographic location. Again, the moisturevalue is the georeferenced value contained in the moisture map 332.Model generator 344 then generates a predictive residue model 350 thatis used by residue map generator 352 to predict the residuecharacteristic at a location in the field based upon the moisture valuefor that location in the field.

Model generator 346 identifies a relationship between the residuecharacteristic in the processed image data 340, at a geographic locationcorresponding to where the image data 340 was georeferenced, and thetopographic characteristic value from the topographic map 333 at thatsame location. Model generator 346 generates a predictive residue model350 that is used by residue map generator 352 to predict the residuecharacteristic at a particular location in the field based upon thetopographic characteristic value at that location in the field.

In light of the above, the predictive model generator 210 is operable toproduce a plurality of predictive residue models, such as one or more ofthe predictive residue models generated by model generators 342, 344,and 346. In another example, two or more of the predictive residuemodels described above may be combined into a single predictive residuemodel that can be used to predict residue characteristics based upon twoor more of the vegetative index, moisture and topographic value atdifferent locations in the field. Any of these residue models, orcombinations thereof, are represented collectively by residue model 350in FIG. 4 .

The predictive residue model 350 is provided to predictive map generator212. In the example of FIG. 4 , predictive map generator 212 includes aresidue map generator 352. In other examples, the predictive mapgenerator 212 may include additional or different map generators. Thus,in some examples, the predictive map generator 212 may include otheritems 358 which may include other types of map generators to generatemaps for other types of characteristics. Residue map generator 352receives the predictive residue model 350 and generates a predictive mapthat predicts the residue characteristics at different locations in thefield based upon values from one or more the vegetative index map 331,moisture map 332 and topographic map 333 and the predictive residuemodel 350.

Predictive map generator 212 outputs one or more predictive residue maps360 that are predictive of one or more residue characteristics, such asresidue spread width, residue thickness, and residue content. Thegenerated predictive residue map 360 may be provided to control zonegenerator 213, control system 214, or both. Control zone generator 213generates control zones and incorporates those control zones into thefunctional predictive map, i.e., predictive map 360, to producepredictive control zone map 265. One or both of predictive map 264 andpredictive control zone map 265 may be provided to control system 214,which generates control signals to control one or more of thecontrollable subsystems 216 based upon the predictive map 264,predictive control zone map 265, or both.

FIG. 5 is a flow diagram of an example of operation of predictive modelgenerator 210 and predictive map generator 212 in generating thepredictive residue model 350 and the predictive residue map 360. Atblock 362, predictive model generator 210 and predictive map generator212 receive vegetative index map 331, moisture map 332, topographic map333, or some combination thereof. At block 364, processing system 338receives one or more sensor signals from residue sensor 336. Asdiscussed above, the residue sensor 336 may be a camera, such asrearward looking camera 366; an optical sensor 368, such as a camera,looking at least partially internal to an agricultural harvester; oranother type of residue sensor 370. For example, other residue sensors370 could include an impact force sensor or other electromagneticsensors.

At block 372, processing system 338 processes the one or more receivedsensor signals to generate data indicative of a residue characteristic.At block 373, the sensor data may be indicative of residue spread.Residue spread can include one or more dimensions relative to thecombine, for instance width relative to, or a distance expelled from, ora distance offset from the agricultural harvester. In some instances, asindicated at block 374, the sensor data may be indicative of residuethickness. Residue thickness is indicative of the depth or amount ofresidue on the surface of the field. In some instances, as indicated atblock 375, the sensor data may be indicative of residue uniformity.Residue uniformity is indicative of the distribution of the residueacross a surface. In some instances, as indicated at block 376, thesensor data may be indicative of residue content. Residue content isindicative of the types of material in the residue (e.g., weeds, cropplants, stalks, weed seeds, grain, etc.) and/or the quality of thematerial (e.g., chopped straw length, seed crush quality, etc.). Thesensor data can include other data as well as indicated by block 377.

At block 382, predictive model generator 210 also obtains the geographiclocation corresponding to the image data. For instance, the predictivemodel generator 210 can obtain the geographic position from geographicposition sensor 204 and determine, based upon machine delays, machinespeed, camera field of view, etc., a precise geographic location wherethe image was taken or from which the image data 340 corresponds to. Insome instances, the pixels in the image are correlated with geographiclocations on the field and the pixel location of the sensed data istranslated into a geographic location on the field.

At block 384, predictive model generator 210 generates one or morepredictive residue models, such as residue model 350, that model arelationship between a value obtained from an information map, such asinformation map 258, and a residue characteristic value being sensed bythe in-situ sensor 208 or a related characteristic. For instance,predictive model generator 210 may generate a predictive residue modelthat models the relationship between a vegetative index value and asensed residue characteristic indicated by the image data obtained fromin-situ sensor 208.

At block 385, the predictive residue model, such as predictive residuemodel 350, is provided to predictive map generator 212 which generates apredictive residue map 360 that maps a predicted residue characteristicbased on the predictive residue model 350 and one or more of thevegetative index map 331, the moisture map 332, and the topographic map333. For instance, in some examples, the predictive residue map 360predicts residue spread, as indicated by block 386. In some examples,the predictive residue map 360 predicts residue thickness, as indicatedby block 387. In some examples, the predictive residue map 360 predictsresidue uniformity, as indicated by block 388. In some examples, thepredictive residue map 360 predicts residue content, as indicated byblock 389. In some examples, the predictive map 360 predicts other itemsor some combination of items, as indicated by block 390. Further, thepredictive residue map 360 can be generated during the course of anagricultural operation. Thus, as an agricultural harvester is movingthrough a field performing an agricultural operation, the predictiveresidue map 360 is generated as the agricultural operation is beingperformed.

At block 394, predictive map generator 212 outputs the predictiveresidue map 360. At block 391 predictive residue map generator 212outputs the predictive residue map for presentation to and possibleinteraction by operator 260. At block 393, predictive map generator 212may configure the map for consumption by control system 214. At block395, predictive map generator 212 can also provide the map 360 tocontrol zone generator 213 for generation of control zones. At block397, predictive map generator 212 configures the predictive residue map360 in other ways as well. The predictive residue map 360 (with orwithout the control zones) is provided to control system 214. At block396, control system 214 generates control signals to control thecontrollable subsystems 216 based upon the predictive residue map 360.

FIG. 6 shows a block diagram illustrating one example of control zonegenerator 213. Control zone generator 213 includes work machine actuator(WMA) selector 486, control zone generation system 488, and regime zonegeneration system 490. Control zone generator 213 may also include otheritems 492. Control zone generation system 488 includes control zonecriteria identifier component 494, control zone boundary definitioncomponent 496, target setting identifier component 498, and other items520. Regime zone generation system 490 includes regime zone criteriaidentification component 522, regime zone boundary definition component524, settings resolver identifier component 526, and other items 528.Before describing the overall operation of control zone generator 213 inmore detail, a brief description of some of the items in control zonegenerator 213 and the respective operations thereof will first beprovided.

Agricultural harvester 100, or other work machines, may have a widevariety of different types of controllable actuators that performdifferent functions. The controllable actuators on agriculturalharvester 100 or other work machines are collectively referred to aswork machine actuators (WMAs). Each WMA may be independentlycontrollable based upon values on a functional predictive map, or theWMAs may be controlled as sets based upon one or more values on afunctional predictive map. Therefore, control zone generator 213 maygenerate control zones corresponding to each individually controllableWMA or corresponding to the sets of WMAs that are controlled incoordination with one another.

WMA selector 486 selects a WMA or a set of WMAs for which correspondingcontrol zones are to be generated. Control zone generation system 488then generates the control zones for the selected WMA or set of WMAs.For each WMA or set of WMAs, different criteria may be used inidentifying control zones. For example, for one WMA, the WMA responsetime may be used as the criteria for defining the boundaries of thecontrol zones. In another example, wear characteristics (e.g., how mucha particular actuator or mechanism wears as a result of movementthereof) may be used as the criteria for identifying the boundaries ofcontrol zones. Control zone criteria identifier component 494 identifiesparticular criteria that are to be used in defining control zones forthe selected WMA or set of WMAs. Control zone boundary definitioncomponent 496 processes the values on a functional predictive map underanalysis to define the boundaries of the control zones on thatfunctional predictive map based upon the values in the functionalpredictive map under analysis and based upon the control zone criteriafor the selected WMA or set of WMAs.

Target setting identifier component 498 sets a value of the targetsetting that will be used to control the WMA or set of WMAs in differentcontrol zones. For instance, if the selected WMA is propulsion system250 and the functional predictive map under analysis is a functionalpredictive speed map 438, then the target setting in each control zonemay be a target speed setting based on speed values contained in thefunctional predictive speed map 238 within the identified control zone.

In some examples, where agricultural harvester 100 is to be controlledbased on a current or future location of the agricultural harvester 100,multiple target settings may be possible for a WMA at a given location.In that case, the target settings may have different values and may becompeting. Thus, the target settings need to be resolved so that only asingle target setting is used to control the WMA. For example, where theWMA is an actuator in propulsion system 250 that is being controlled inorder to control the speed of agricultural harvester 100, multipledifferent competing sets of criteria may exist that are considered bycontrol zone generation system 488 in identifying the control zones andthe target settings for the selected WMA in the control zones. Forinstance, different target settings for controlling machine speed may begenerated based upon, for example, a detected or predicted feed ratevalue, a detected or predictive fuel efficiency value, a detected orpredicted grain loss value, or a combination of these. However, at anygiven time, the agricultural harvester 100 cannot travel over the groundat multiple speeds simultaneously. Rather, at any given time, theagricultural harvester 100 travels at a single speed. Thus, one of thecompeting target settings is selected to control the speed ofagricultural harvester 100.

Therefore, in some examples, regime zone generation system 490 generatesregime zones to resolve multiple different competing target settings.Regime zone criteria identification component 522 identifies thecriteria that are used to establish regime zones for the selected WMA orset of WMAs on the functional predictive map under analysis. Somecriteria that can be used to identify or define regime zones include,for example, crop type or crop variety based on an as-planted map oranother source of the crop type or crop variety, weed type, weedintensity, or crop state, such as whether the crop is down, partiallydown or standing. Just as each WMA or set of WMAs may have acorresponding control zone, different WMAs or sets of WMAs may have acorresponding regime zone. Regime zone boundary definition component 524identifies the boundaries of regime zones on the functional predictivemap under analysis based on the regime zone criteria identified byregime zone criteria identification component 522.

In some examples, regime zones may overlap with one another. Forinstance, a crop variety regime zone may overlap with a portion of or anentirety of a crop state regime zone. In such an example, the differentregime zones may be assigned to a precedence hierarchy so that, wheretwo or more regime zones overlap, the regime zone assigned with agreater hierarchical position or importance in the precedence hierarchyhas precedence over the regime zones that have lesser hierarchicalpositions or importance in the precedence hierarchy. The precedencehierarchy of the regime zones may be manually set or may beautomatically set using a rules-based system, a model-based system, oranother system. As one example, where a downed crop regime zone overlapswith a crop variety regime zone, the downed crop regime zone may beassigned a greater importance in the precedence hierarchy than the cropvariety regime zone so that the downed crop regime zone takesprecedence.

In addition, each regime zone may have a unique settings resolver for agiven WMA or set of WMAs. Settings resolver identifier component 526identifies a particular settings resolver for each regime zoneidentified on the functional predictive map under analysis and aparticular settings resolver for the selected WMA or set of WMAs.

Once the settings resolver for a particular regime zone is identified,that settings resolver may be used to resolve competing target settings,where more than one target setting is identified based upon the controlzones. The different types of settings resolvers can have differentforms. For instance, the settings resolvers that are identified for eachregime zone may include a human choice resolver in which the competingtarget settings are presented to an operator or other user forresolution. In another example, the settings resolver may include aneural network or other artificial intelligence or machine learningsystem. In such instances, the settings resolvers may resolve thecompeting target settings based upon a predicted or historic qualitymetric corresponding to each of the different target settings. As anexample, an increased vehicle speed setting may reduce the time toharvest a field and reduce corresponding time-based labor and equipmentcosts but may increase grain losses. A reduced vehicle speed setting mayincrease the time to harvest a field and increase correspondingtime-based labor and equipment costs but may decrease grain losses. Whengrain loss or time to harvest is selected as a quality metric, thepredicted or historic value for the selected quality metric, given thetwo competing vehicle speed settings values, may be used to resolve thespeed setting. In some instances, the settings resolvers may be a set ofthreshold rules that may be used instead of, or in addition to, theregime zones. An example of a threshold rule may be expressed asfollows:

-   -   If predicted biomass values within 20 feet of the header of the        agricultural harvester 100 are greater that x kilograms (where x        is a selected or predetermined value), then use the target        setting value that is chosen based on feed rate over other        competing target settings, otherwise use the target setting        value based on grain loss over other competing target setting        values.

The settings resolvers may be logical components that execute logicalrules in identifying a target setting. For instance, the settingsresolver may resolve target settings while attempting to minimizeharvest time or minimize the total harvest cost or maximize harvestedgrain or based on other variables that are computed as a function of thedifferent candidate target settings. A harvest time may be minimizedwhen an amount to complete a harvest is reduced to at or below aselected threshold. A total harvest cost may be minimized where thetotal harvest cost is reduced to at or below a selected threshold.Harvested grain may be maximized where the amount of harvested grain isincreased to at or above a selected threshold.

FIG. 7 is a flow diagram illustrating one example of the operation ofcontrol zone generator 213 in generating control zones and regime zonesfor a map that the control zone generator 213 receives for zoneprocessing (e.g., for a map under analysis).

At block 530, control zone generator 213 receives a map under analysisfor processing. In one example, as shown at block 532, the map underanalysis is a functional predictive map. For example, the map underanalysis may be one of the functional predictive maps 436, 437, 438, or440. Block 534 indicates that the map under analysis can be other mapsas well.

At block 536, WMA selector 486 selects a WMA or a set of WMAs for whichcontrol zones are to be generated on the map under analysis. At block538, control zone criteria identification component 494 obtains controlzone definition criteria for the selected WMAs or set of WMAs. Block 540indicates an example in which the control zone criteria are or includewear characteristics of the selected WMA or set of WMAs. Block 542indicates an example in which the control zone definition criteria areor include a magnitude and variation of input source data, such as themagnitude and variation of the values on the map under analysis or themagnitude and variation of inputs from various in-situ sensors 208.Block 544 indicates an example in which the control zone definitioncriteria are or include physical machine characteristics, such as thephysical dimensions of the machine, a speed at which differentsubsystems operate, or other physical machine characteristics. Block 546indicates an example in which the control zone definition criteria areor include a responsiveness of the selected WMA or set of WMAs inreaching newly commanded setting values. Block 548 indicates an examplein which the control zone definition criteria are or include machineperformance metrics. Block 550 indicates an example in which the controlzone definition criteria are or includes operator preferences. Block 552indicates an example in which the control zone definition criteria areor include other items as well. Block 549 indicates an example in whichthe control zone definition criteria are time based, meaning thatagricultural harvester 100 will not cross the boundary of a control zoneuntil a selected amount of time has elapsed since agricultural harvester100 entered a particular control zone. In some instances, the selectedamount of time may be a minimum amount of time. Thus, in some instances,the control zone definition criteria may prevent the agriculturalharvester 100 from crossing a boundary of a control zone until at leastthe selected amount of time has elapsed. Block 551 indicates an examplein which the control zone definition criteria are based on a selectedsize value. For example, a control zone definition criteria that isbased on a selected size value may preclude definition of a control zonethat is smaller than the selected size. In some instances, the selectedsize may be a minimum size.

At block 554, regime zone criteria identification component 522 obtainsregime zone definition criteria for the selected WMA or set of WMAs.Block 556 indicates an example in which the regime zone definitioncriteria are based on a manual input from operator 260 or another user.Block 558 illustrates an example in which the regime zone definitioncriteria are based on crop type or crop variety. Block 560 illustratesan example in which the regime zone definition criteria are based onweed type or weed intensity or both. Block 562 illustrates an example inwhich the regime zone definition criteria are based on or include cropstate. Block 564 indicates an example in which the regime zonedefinition criteria are or include other criteria as well. For example,the regime zone definition criteria are based on or include topographiccharacteristics.

At block 566, control zone boundary definition component 496 generatesthe boundaries of control zones on the map under analysis based upon thecontrol zone criteria. Regime zone boundary definition component 524generates the boundaries of regime zones on the map under analysis basedupon the regime zone criteria. Block 568 indicates an example in whichthe zone boundaries are identified for the control zones and the regimezones. Block 570 shows that target setting identifier component 498identifies the target settings for each of the control zones. Thecontrol zones and regime zones can be generated in other ways as well,and this is indicated by block 572.

At block 574, settings resolver identifier component 526 identifies thesettings resolver for the selected WMAs in each regime zone defined byregimes zone boundary definition component 524. As discussed above, theregime zone resolver can be a human resolver 576, an artificialintelligence or machine learning system resolver 578, a resolver 580based on predicted or historic quality for each competing targetsetting, a rules-based resolver 582, a performance criteria-basedresolver 584, or other resolvers 586.

At block 588, WMA selector 486 determines whether there are more WMAs orsets of WMAs to process. If additional WMAs or sets of WMAs areremaining to be processed, processing reverts to block 436 where thenext WMA or set of WMAs for which control zones and regime zones are tobe defined is selected. When no additional WMAs or sets of WMAs forwhich control zones or regime zones are to be generated are remaining,processing moves to block 590 where control zone generator 213 outputs amap with control zones, target settings, regime zones, and settingsresolvers for each of the WMAs or sets of WMAs. As discussed above, theoutputted map can be presented to operator 260 or another user; theoutputted map can be provided to control system 214; or the outputtedmap can be output in other ways.

FIG. 8 illustrates one example of the operation of control system 214 incontrolling agricultural harvester 100 based upon a map that is outputby control zone generator 213. Thus, at block 592, control system 214receives a map of the worksite. In some instances, the map can be afunctional predictive map that may include control zones and regimezones, as represented by block 594. In some instances, the received mapmay be a functional predictive map that excludes control zones andregime zones. Block 596 indicates an example in which the received mapof the worksite can be an information map having control zones andregime zones identified on it. Block 598 indicates an example in whichthe received map can include multiple different maps or multipledifferent map layers. Block 610 indicates an example in which thereceived map can take other forms as well.

At block 612, control system 214 receives a sensor signal fromgeographic position sensor 204. The sensor signal from geographicposition sensor 204 can include data that indicates the geographiclocation 614 of agricultural harvester 100, the speed 616 ofagricultural harvester 100, the heading 618 or agricultural harvester100, or other information 620. At block 622, zone controller 247 selectsa regime zone, and, at block 624, zone controller 247 selects a controlzone on the map based on the geographic position sensor signal. At block626, zone controller 247 selects a WMA or a set of WMAs to becontrolled. At block 628, zone controller 247 obtains one or more targetsettings for the selected WMA or set of WMAs. The target settings thatare obtained for the selected WMA or set of WMAs may come from a varietyof different sources. For instance, block 630 shows an example in whichone or more of the target settings for the selected WMA or set of WMAsis based on an input from the control zones on the map of the worksite.Block 632 shows an example in which one or more of the target settingsis obtained from human inputs from operator 260 or another user. Block634 shows an example in which the target settings are obtained from anin-situ sensor 208. Block 636 shows an example in which the one or moretarget settings is obtained from one or more sensors on other machinesworking in the same field either concurrently with agriculturalharvester 100 or from one or more sensors on machines that worked in thesame field in the past. Block 638 shows an example in which the targetsettings are obtained from other sources as well.

At block 640, zone controller 247 accesses the settings resolver for theselected regime zone and controls the settings resolver to resolvecompeting target settings into a resolved target setting. As discussedabove, in some instances, the settings resolver may be a human resolverin which case zone controller 247 controls operator interface mechanisms218 to present the competing target settings to operator 260 or anotheruser for resolution. In some instances, the settings resolver may be aneural network or other artificial intelligence or machine learningsystem, and zone controller 247 submits the competing target settings tothe neural network, artificial intelligence, or machine learning systemfor selection. In some instances, the settings resolver may be based ona predicted or historic quality metric, on threshold rules, or onlogical components. In any of these latter examples, zone controller 247executes the settings resolver to obtain a resolved target setting basedon the predicted or historic quality metric, based on the thresholdrules, or with the use of the logical components.

At block 642, with zone controller 247 having identified the resolvedtarget setting, zone controller 247 provides the resolved target settingto other controllers in control system 214, which generate and applycontrol signals to the selected WMA or set of WMAs based upon theresolved target setting. For instance, where the selected WMA is amachine or header actuator 248, zone controller 247 provides theresolved target setting to settings controller 232 or header/realcontroller 238 or both to generate control signals based upon theresolved target setting, and those generated control signals are appliedto the machine or header actuators 248. At block 644, if additional WMAsor additional sets of WMAs are to be controlled at the currentgeographic location of the agricultural harvester 100 (as detected atblock 612), then processing reverts to block 626 where the next WMA orset of WMAs is selected. The processes represented by blocks 626 through644 continue until all of the WMAs or sets of WMAs to be controlled atthe current geographical location of the agricultural harvester 100 havebeen addressed. If no additional WMAs or sets of WMAs are to becontrolled at the current geographic location of the agriculturalharvester 100 remain, processing proceeds to block 646 where zonecontroller 247 determines whether additional control zones to beconsidered exist in the selected regime zone. If additional controlzones to be considered exist, processing reverts to block 624 where anext control zone is selected. If no additional control zones areremaining to be considered, processing proceeds to block 648 where adetermination as to whether additional regime zones are remaining to beconsider. Zone controller 247 determines whether additional regime zonesare remaining to be considered. If additional regimes zone are remainingto be considered, processing reverts to block 622 where a next regimezone is selected.

At block 650, zone controller 247 determines whether the operation thatagricultural harvester 100 is performing is complete. If not, the zonecontroller 247 determines whether a control zone criterion has beensatisfied to continue processing, as indicated by block 652. Forinstance, as mentioned above, control zone definition criteria mayinclude criteria defining when a control zone boundary may be crossed bythe agricultural harvester 100. For example, whether a control zoneboundary may be crossed by the agricultural harvester 100 may be definedby a selected time period, meaning that agricultural harvester 100 isprevented from crossing a zone boundary until a selected amount of timehas transpired. In that case, at block 652, zone controller 247determines whether the selected time period has elapsed. Additionally,zone controller 247 can perform processing continually. Thus, zonecontroller 247 does not wait for any particular time period beforecontinuing to determine whether an operation of the agriculturalharvester 100 is completed. At block 652, zone controller 247 determinesthat it is time to continue processing, then processing continues atblock 612 where zone controller 247 again receives an input fromgeographic position sensor 204. It will also be appreciated that zonecontroller 247 can control the WMAs and sets of WMAs simultaneouslyusing a multiple-input, multiple-output controller instead ofcontrolling the WMAs and sets of WMAs sequentially.

FIG. 9 is a block diagram showing one example of an operator interfacecontroller 231. In an illustrated example, operator interface controller231 includes operator input command processing system 654, othercontroller interaction system 656, speech processing system 658, andaction signal generator 660. Operator input command processing system654 includes speech handling system 662, touch gesture handling system664, and other items 666. Other controller interaction system 656includes controller input processing system 668 and controller outputgenerator 670. Speech processing system 658 includes trigger detector672, recognition component 674, synthesis component 676, naturallanguage understanding system 678, dialog management system 680, andother items 682. Action signal generator 660 includes visual controlsignal generator 684, audio control signal generator 686, haptic controlsignal generator 688, and other items 690. Before describing operationof the example operator interface controller 231 shown in FIG. 9 inhandling various operator interface actions, a brief description of someof the items in operator interface controller 231 and the associatedoperation thereof is first provided.

Operator input command processing system 654 detects operator inputs onoperator interface mechanisms 218 and processes those inputs forcommands. Speech handling system 662 detects speech inputs and handlesthe interactions with speech processing system 658 to process the speechinputs for commands. Touch gesture handling system 664 detects touchgestures on touch sensitive elements in operator interface mechanisms218 and processes those inputs for commands.

Other controller interaction system 656 handles interactions with othercontrollers in control system 214. Controller input processing system668 detects and processes inputs from other controllers in controlsystem 214, and controller output generator 670 generates outputs andprovides those outputs to other controllers in control system 214.Speech processing system 658 recognizes speech inputs, determines themeaning of those inputs, and provides an output indicative of themeaning of the spoken inputs. For instance, speech processing system 658may recognize a speech input from operator 260 as a settings changecommand in which operator 260 is commanding control system 214 to changea setting for a controllable subsystem 216. In such an example, speechprocessing system 658 recognizes the content of the spoken command,identifies the meaning of that command as a settings change command, andprovides the meaning of that input back to speech handling system 662.Speech handling system 662, in turn, interacts with controller outputgenerator 670 to provide the commanded output to the appropriatecontroller in control system 214 to accomplish the spoken settingschange command.

Speech processing system 658 may be invoked in a variety of differentways. For instance, in one example, speech handling system 662continuously provides an input from a microphone (being one of theoperator interface mechanisms 218) to speech processing system 658. Themicrophone detects speech from operator 260, and the speech handlingsystem 662 provides the detected speech to speech processing system 658.Trigger detector 672 detects a trigger indicating that speech processingsystem 658 is invoked. In some instances, when speech processing system658 is receiving continuous speech inputs from speech handling system662, speech recognition component 674 performs continuous speechrecognition on all speech spoken by operator 260. In some instances,speech processing system 658 is configured for invocation using a wakeupword. That is, in some instances, operation of speech processing system658 may be initiated based on recognition of a selected spoken word,referred to as the wakeup word. In such an example, where recognitioncomponent 674 recognizes the wakeup word, the recognition component 674provides an indication that the wakeup word has been recognized totrigger detector 672. Trigger detector 672 detects that speechprocessing system 658 has been invoked or triggered by the wakeup word.In another example, speech processing system 658 may be invoked by anoperator 260 actuating an actuator on a user interface mechanism, suchas by touching an actuator on a touch sensitive display screen, bypressing a button, or by providing another triggering input. In such anexample, trigger detector 672 can detect that speech processing system658 has been invoked when a triggering input via a user interfacemechanism is detected. Trigger detector 672 can detect that speechprocessing system 658 has been invoked in other ways as well.

Once speech processing system 658 is invoked, the speech input fromoperator 260 is provided to speech recognition component 674. Speechrecognition component 674 recognizes linguistic elements in the speechinput, such as words, phrases, or other linguistic units. Naturallanguage understanding system 678 identifies a meaning of the recognizedspeech. The meaning may be a natural language output, a command outputidentifying a command reflected in the recognized speech, a value outputidentifying a value in the recognized speech, or any of a wide varietyof other outputs that reflect the understanding of the recognizedspeech. For example, the natural language understanding system 678 andspeech processing system 568, more generally, may understand of themeaning of the recognized speech in the context of agriculturalharvester 100.

In some examples, speech processing system 658 can also generate outputsthat navigate operator 260 through a user experience based on the speechinput. For instance, dialog management system 680 may generate andmanage a dialog with the user in order to identify what the user wishesto do. The dialog may disambiguate a user's command; identify one ormore specific values that are needed to carry out the user's command; orobtain other information from the user or provide other information tothe user or both. Synthesis component 676 may generate speech synthesiswhich can be presented to the user through an audio operator interfacemechanism, such as a speaker. Thus, the dialog managed by dialogmanagement system 680 may be exclusively a spoken dialog or acombination of both a visual dialog and a spoken dialog.

Action signal generator 660 generates action signals to control operatorinterface mechanisms 218 based upon outputs from one or more of operatorinput command processing system 654, other controller interaction system656, and speech processing system 658. Visual control signal generator684 generates control signals to control visual items in operatorinterface mechanisms 218. The visual items may be lights, a displayscreen, warning indicators, or other visual items. Audio control signalgenerator 686 generates outputs that control audio elements of operatorinterface mechanisms 218. The audio elements include a speaker, audiblealert mechanisms, horns, or other audible elements. Haptic controlsignal generator 688 generates control signals that are output tocontrol haptic elements of operator interface mechanisms 218. The hapticelements include vibration elements that may be used to vibrate, forexample, the operator's seat, the steering wheel, pedals, or joysticksused by the operator. The haptic elements may include tactile feedbackor force feedback elements that provide tactile feedback or forcefeedback to the operator through operator interface mechanisms. Thehaptic elements may include a wide variety of other haptic elements aswell.

FIG. 10 is a flow diagram illustrating one example of the operation ofoperator interface controller 231 in generating an operator interfacedisplay on an operator interface mechanism 218, which can include atouch sensitive display screen. FIG. 10 also illustrates one example ofhow operator interface controller 231 can detect and process operatorinteractions with the touch sensitive display screen.

At block 692, operator interface controller 231 receives a map. Block694 indicates an example in which the map is a functional predictivemap, and block 696 indicates an example in which the map is another typeof map. At block 698, operator interface controller 231 receives aninput from geographic position sensor 204 identifying the geographiclocation of the agricultural harvester 100. As indicated in block 700,the input from geographic position sensor 204 can include the heading,along with the location, of agricultural harvester 100. Block 702indicates an example in which the input from geographic position sensor204 includes the speed of agricultural harvester 100, and block 704indicates an example in which the input from geographic position sensor204 includes other items.

At block 706, visual control signal generator 684 in operator interfacecontroller 231 controls the touch sensitive display screen in operatorinterface mechanisms 218 to generate a display showing all or a portionof a field represented by the received map. Block 708 indicates that thedisplayed field can include a current position marker showing a currentposition of the agricultural harvester 100 relative to the field. Block710 indicates an example in which the displayed field includes a nextwork unit marker that identifies a next work unit (or area on the field)in which agricultural harvester 100 will be operating. Block 712indicates an example in which the displayed field includes an upcomingarea display portion that displays areas that are yet to be processed byagricultural harvester 100, and block 714 indicates an example in whichthe displayed field includes previously visited display portions thatrepresent areas of the field that agricultural harvester 100 has alreadyprocessed. Block 716 indicates an example in which the displayed fielddisplays various characteristics of the field having georeferencedlocations on the map. For instance, if the received map is a residuemap, the displayed field may show the different chopped straw lengthsexisting in the field georeferenced within the displayed field. Themapped characteristics can be shown in the previously visited areas (asshown in block 714), in the upcoming areas (as shown in block 712), andin the next work unit (as shown in block 710). Block 718 indicates anexample in which the displayed field includes other items as well.

FIG. 11 is a pictorial illustration showing one example of a userinterface display 720 that can be generated on a touch sensitive displayscreen. In other implementations, the user interface display 720 may begenerated on other types of displays. The touch sensitive display screenmay be mounted in the operator compartment of agricultural harvester 100or on the mobile device or elsewhere. User interface display 720 will bedescribed prior to continuing with the description of the flow diagramshown in FIG. 10 .

In the example shown in FIG. 11 , user interface display 720 illustratesthat the touch sensitive display screen includes a display feature foroperating a microphone 722 and a speaker 724. Thus, the touch sensitivedisplay may be communicably coupled to the microphone 722 and thespeaker 724. Block 726 indicates that the touch sensitive display screencan include a wide variety of user interface control actuators, such asbuttons, keypads, soft keypads, links, icons, switches, etc. Theoperator 260 can actuator the user interface control actuators toperform various functions.

In the example shown in FIG. 11 , user interface display 720 includes afield display portion 728 that displays at least a portion of the fieldin which the agricultural harvester 100 is operating. The field displayportion 728 is shown with a current position marker 708 that correspondsto a current position of agricultural harvester 100 in the portion ofthe field shown in field display portion 728. In one example, theoperator may control the touch sensitive display in order to zoom intoportions of field display portion 728 or to pan or scroll the fielddisplay portion 728 to show different portions of the field. A next workunit 730 is shown as an area of the field directly in front of thecurrent position marker 708 of agricultural harvester 100. The currentposition marker 708 may also be configured to identify the direction oftravel of agricultural harvester 100, a speed of travel of agriculturalharvester 100 or both. In FIG. 13 , the shape of the current positionmarker 708 provides an indication as to the orientation of theagricultural harvester 100 within the field which may be used as anindication of a direction of travel of the agricultural harvester 100.

The size of the next work unit 730 marked on field display portion 728may vary based upon a wide variety of different criteria. For instance,the size of next work unit 730 may vary based on the speed of travel ofagricultural harvester 100. Thus, when the agricultural harvester 100 istraveling faster, then the area of the next work unit 730 may be largerthan the area of next work unit 730 if agricultural harvester 100 istraveling more slowly. In another example, the size of the next workunit 730 may vary based on the dimensions of the agricultural harvester100, including equipment on agricultural harvester 100 (such as header102). For example, the width of the next work unit 730 may vary based ona width of header 102. Field display portion 728 is also showndisplaying previously visited area 714 and upcoming areas 712.Previously visited areas 714 represent areas that are already harvestedwhile upcoming areas 712 represent areas that still need to beharvested. The field display portion 728 is also shown displayingdifferent characteristics of the field. In the example illustrated inFIG. 11 , the map that is being displayed is a predictive straw lengthmap. Therefore, a plurality of different straw length markers aredisplayed on field display portion 728. There are a set of straw lengthdisplay markers 732 shown in the already visited areas 714. There arealso a set of straw length display markers 732 shown in the upcomingareas 712, and there are a set of straw length display markers 732 shownin the next work unit 730. FIG. 11 shows that the straw length displaymarkers 732 are made up of different symbols that indicate an areahaving similar straw lengths. In the example shown in FIG. 3 , the !symbol represents areas containing straw having long straw length; the *symbol represents areas containing straw having medium straw length; andthe # symbol represents an area containing straw having short strawlength. Thus, the field display portion 728 shows different measured orpredicted values (or characteristics indicated by the values) that arelocated at different areas within the field and represents thosemeasured or predicted values (or characteristics indicated by thevalues) with a variety of display markers 732. As shown, the fielddisplay portion 728 includes display markers, particularly straw lengthdisplay markers 732 in the illustrated example of FIG. 11 , atparticular locations associated with particular locations on the fieldbeing displayed. In some instances, each location of the field may havea display marker associated therewith. Thus, in some instances, adisplay marker may be provided at each location of the field displayportion 728 to identify the nature of the characteristic being mappedfor each particular location of the field. Consequently, the presentdisclosure encompasses providing a display marker, such as the strawlength display marker 732 (as in the context of the present example ofFIG. 11 ), at one or more locations on the field display portion 728 toidentify the nature, degree, etc., of the characteristic beingdisplayed, thereby identifying the characteristic at the correspondinglocation in the field being displayed. As described earlier, the displaymarkers 732 may be made up of different symbols, and, as describedbelow, the symbols may be any display feature such as different colors,shapes, patterns, intensities, text, icons, or other display features.In some instances, each location of the field may have a display markerassociated therewith. Thus, in some instances, a display marker may beprovided at each location of the field display portion 728 to identifythe nature of the characteristic being mapped for each particularlocation of the field. Consequently, the present disclosure encompassesproviding a display marker, such as the loss level display marker 732(as in the context of the present example of FIG. 11 ), at one or morelocations on the field display portion 728 to identify the nature,degree, etc., of the characteristic being displayed, thereby identifyingthe characteristic at the corresponding location in the field beingdisplayed.

In other examples, the map being displayed may be one or more of themaps described herein, including information maps, information maps, thefunctional predictive maps, such as predictive maps or predictivecontrol zone maps, or a combination thereof. Thus, the markers andcharacteristics being displayed will correlate to the information, data,characteristics, and values provided by the one or more maps beingdisplayed.

In the example of FIG. 11 , user interface display 720 also has acontrol display portion 738. Control display portion 738 allows theoperator to view information and to interact with user interface display720 in various ways.

The actuators and display markers in portion 738 may be displayed as,for example, individual items, fixed lists, scrollable lists, drop downmenus, or drop down lists. In the example shown in FIG. 11 , displayportion 738 shows information for the three different straw lengths thatcorrespond to the three symbols mentioned above. Display portion 738also includes a set of touch sensitive actuators with which the operator260 can interact by touch. For example, the operator 260 may touch thetouch sensitive actuators with a finger to activate the respective touchsensitive actuator.

As shown in FIG. 11 , display portion 738 includes an interactive flagdisplay portion, indicated generally at 741. Interactive flag displayportion 741 includes a flag column 739 that shows flags that have beenautomatically or manually set. Flag actuator 740 allows operator 260 tomark a location, such as the current location of the agriculturalharvester, or another location on the field designated by the operatorand add information indicating the straw length found at the currentlocation. For instance, when the operator 260 actuates the flag actuator740 by touching the flag actuator 740, touch gesture handling system 664in operator interface controller 231 identifies the current location asone where agricultural harvester 100 generated long straw length. Whenthe operator 260 touches the button 742, touch gesture handling system664 identifies the current location as a location where agriculturalharvester 100 generated medium straw length. When the operator 260touches the button 744, touch gesture handling system 664 identifies thecurrent location as a location where agricultural harvester 100generated short straw length. Upon actuation of one of the flagactuators 740, 742, or 744, touch gesture handling system 664 cancontrol visual control signal generator 684 to add a symbolcorresponding to the identified straw length on field display portion728 at a location the user identifies. In this way, areas of the fieldwhere the predicted value did not accurately represent an actual valuecan be marked for later analysis, and can also be used in machinelearning. In other examples, the operator may designate areas ahead ofor around the agricultural harvester 100 by actuating one of the flagactuators 740, 742, or 744 such that control of the agriculturalharvester 100 can be undertaken based on the value designated by theoperator 260.

Display portion 738 also includes an interactive marker display portion,indicated generally at 743. Interactive marker display portion 743includes a symbol column 746 that displays the symbols corresponding toeach category of values or characteristics (in the case of FIG. 11 ,straw length) that is being tracked on the field display portion 728.Display portion 738 also includes an interactive designator displayportion, indicated generally at 745. Interactor designator displayportion 745 includes a designator column 748 that shows the designator(which may be a textual designator or other designator) identifying thecategory of values or characteristics (in the case of FIG. 11 , strawlength). Without limitation, the symbols in symbol column 746 and thedesignators in designator column 748 can include any display featuresuch as different colors, shapes, patterns, intensities, text, icons, orother display features, and can be customizable by interaction of anoperator of agricultural harvester 100.

Display portion 738 also includes an interactive value display portion,indicated generally at 747. Interactive value display portion 747includes a value display column 750 that displays selected values. Theselected values correspond to the characteristics or values beingtracked or displayed, or both, on field display portion 728. Theselected values can be selected by an operator of the agriculturalharvester 100. The selected values in value display column 750 define arange of values or a value by which other values, such as predictedvalues, are to be classified. The selected values in value displaycolumn 750 are adjustable by an operator of agricultural harvester 100.In one example, the operator 260 can select the particular part of fielddisplay portion 728 for which the values in column 750 are to bedisplayed. Thus, the values in column 750 can correspond to values indisplay portions 712, 714 or 730.

Display portion 738 also includes an interactive threshold displayportion, indicated generally at 749. Interactive threshold displayportion 749 includes a threshold value display column 752 that displaysaction threshold values. Action threshold values in column 752 may bethreshold values corresponding to the selected values in value displaycolumn 750. If the predicted or measured values of characteristics beingtracked or displayed, or both, satisfy the corresponding actionthreshold values in threshold value display column 752, then controlsystem 214 takes the action identified in column 754. In some instances,a measured or predicted value may satisfy a corresponding actionthreshold value by meeting or exceeding the corresponding actionthreshold value. In one example, operator 260 can select a thresholdvalue, for example, in order to change the threshold value by touchingthe threshold value in threshold value display column 752. Onceselected, the operator 260 may change the threshold value. The thresholdvalues in column 752 can be configured such that the designated actionis performed when the measured or predicted value of the characteristicexceeds the threshold value, equals the threshold value, or is less thanthe threshold value. In some instances, the threshold value mayrepresent a range of values, or range of deviation from the selectedvalues in value display column 750, such that a predicted or measuredcharacteristic value that meets or falls within the range satisfies thethreshold value. For instance, in the example of FIG. 11 , a predictedvalue that falls within 20 mm of 200 mm will satisfy the correspondingaction threshold value and an action, such as adjust chopper settings,will be taken by control system 214. In other examples, the thresholdvalues in column threshold value display column 752 are separate fromthe selected values in value display column 750, such that the values invalue display column 750 define the classification and display ofpredicted or measured values, while the action threshold values definewhen an action is to be taken based on the measured or predicted values.For example, while a predicted or measured straw length value of 100 mmmay be designated as a “medium straw length” for purposes ofclassification and display, the action threshold value may be 10 m suchthat no action will be taken until the straw length value satisfies thethreshold value. In other examples, the threshold values in thresholdvalue display column 752 may include distances or times. For instance,in the example of a distance, the threshold value may be a thresholddistance from the area of the field where the measured or predictedvalue is georeferenced that the agricultural harvester 100 must bebefore an action is taken. For example, a threshold distance value of 10feet would mean that an action will be taken when the agriculturalharvester is at or within 10 feet of the area of the field where themeasured or predicted value is georeferenced. In an example where thethreshold value is time, the threshold value may be a threshold time forthe agricultural harvester 100 to reach the area of the field where themeasured or predictive value is georeferenced. For instance, a thresholdvalue of 5 seconds would mean that an action will be taken when theagricultural harvester 100 is 5 seconds away from the area of the fieldwhere the measured or predicted value is georeferenced. In such anexample, the current location and travel speed of the agriculturalharvester can be accounted for.

Display portion 738 also includes an interactive action display portion,indicated generally at 751. Interactive action display portion 751includes an action display column 754 that displays action identifiersthat indicated actions to be taken when a predicted or measured valuesatisfies an action threshold value in threshold value display column752. Operator 260 can touch the action identifiers in column 754 tochange the action that is to be taken. When a threshold is satisfied, anaction may be taken. For instance, at the bottom of column 754, anincrease cleaning fan speed action and a reduce cleaning fan speedaction are identified as actions that will be taken if the measuredvalue in column 750 meets the threshold value in column 752. In someexamples, then a threshold is met, multiple actions may be taken. Forinstance, a cleaning fan speed may be adjusted, a threshing rotor speedmay be adjusted, and a concave clearance may be adjusted in response toa threshold being satisfied.

The actions that can be set in column 754 can be any of a wide varietyof different types of actions. For example, the actions can include akeep out action which, when executed, inhibits agricultural harvester100 from further harvesting in an area. The actions can include a speedchange action which, when executed, changes the travel speed ofagricultural harvester 100 through the field. The actions can include asetting change action for changing a setting of an internal actuator oranother WMA or set of WMAs or for implementing a settings change actionthat changes a setting of a threshing rotor speed, a cleaning fan speed,a position (e.g., tilt, height, roll, etc.) of the header, along withvarious other settings. These are examples only, and a wide variety ofother actions are contemplated herein.

The items shown on user interface display 720 can be visuallycontrolled. Visually controlling the interface display 720 may beperformed to capture the attention of operator 260. For instance, thedisplay markers can be controlled to modify the intensity, color, orpattern with which the display markers are displayed. Additionally, thedisplay markers may be controlled to flash. The described alterations tothe visual appearance of the display markers are provided as examples.Consequently, other aspects of the visual appearance of the displaymarkers may be altered. Therefore, the display markers can be modifiedunder various circumstances in a desired manner in order, for example,to capture the attention of operator 260. Additionally, while aparticular number of items are shown on user interface display 720, thisneed not be the case. In other examples, more or less items, includingmore or less of a particular item can be included on user interfacedisplay 720.

Returning now to the flow diagram of FIG. 10 , the description of theoperation of operator interface controller 231 continues. At block 760,operator interface controller 231 detects an input setting a flag andcontrols the touch sensitive user interface display 720 to display theflag on field display portion 728. The detected input may be an operatorinput, as indicated at 762, or an input from another controller, asindicated at 764. At block 766, operator interface controller 231detects an in-situ sensor input indicative of a measured characteristicof the field from one of the in-situ sensors 208. At block 768, visualcontrol signal generator 684 generates control signals to control userinterface display 720 to display actuators for modifying user interfacedisplay 720 and for modifying machine control. For instance, block 770represents that one or more of the actuators for setting or modifyingthe values in columns 739, 746, and 748 can be displayed. Thus, the usercan set flags and modify characteristics of those flags. For example, auser can modify the straw lengths and straw length designatorscorresponding to the flags. Block 772 represents that action thresholdvalues in column 752 are displayed. Block 776 represents that theactions in column 754 are displayed, and block 778 represents that themeasured in-situ data in column 750 is displayed. Block 780 indicatesthat a wide variety of other information and actuators can be displayedon user interface display 720 as well.

At block 782, operator input command processing system 654 detects andprocesses operator inputs corresponding to interactions with the userinterface display 720 performed by the operator 260. Where the userinterface mechanism on which user interface display 720 is displayed isa touch sensitive display screen, interaction inputs with the touchsensitive display screen by the operator 260 can be touch gestures 784.In some instances, the operator interaction inputs can be inputs using apoint and click device 786 or other operator interaction inputs 788.

At block 790, operator interface controller 231 receives signalsindicative of an alert condition. For instance, block 792 indicates thatsignals may be received by controller input processing system 668indicating that detected values in column 750 satisfy thresholdconditions present in column 752. As explained earlier, the thresholdconditions may include values being below a threshold, at a threshold,or above a threshold. Block 794 shows that action signal generator 660can, in response to receiving an alert condition, alert the operator 260by using visual control signal generator 684 to generate visual alerts,by using audio control signal generator 686 to generate audio alerts, byusing haptic control signal generator 688 to generate haptic alerts, orby using any combination of these. Similarly, as indicated by block 796,controller output generator 670 can generate outputs to othercontrollers in control system 214 so that those controllers perform thecorresponding action identified in column 754. Block 798 shows thatoperator interface controller 231 can detect and process alertconditions in other ways as well.

Block 900 shows that speech handling system 662 may detect and processinputs invoking speech processing system 658. Block 902 shows thatperforming speech processing may include the use of dialog managementsystem 680 to conduct a dialog with the operator 260. Block 904 showsthat the speech processing may include providing signals to controlleroutput generator 670 so that control operations are automaticallyperformed based upon the speech inputs.

Table 1, below, shows an example of a dialog between operator interfacecontroller 231 and operator 260. In Table 1, operator 260 uses a triggerword or a wakeup word that is detected by trigger detector 672 to invokespeech processing system 658. In the example shown in Table 1, thewakeup word is “Johnny”.

TABLE 1 Operator: “Johnny, tell me about current weed seeds in theresidue” Operator Interface Controller: “Waterhemp 55%. Green foxtail25%. Giant Ragweed is at 20%.”

Table 2 shows an example in which speech synthesis component 676provides an output to audio control signal generator 686 to provideaudible updates on an intermittent or periodic basis. The intervalbetween updates may be time-based, such as every five minutes, orcoverage or distance-based, such as every five acres, orexception-based, such as when a measured value is greater than athreshold value.

TABLE 2 Operator Interface Controller: “Over last 10 minutes, strawlength has averaged 150 mm”

The example shown in Table 3 illustrates that some actuators or userinput mechanisms on the touch sensitive display 720 can be supplementedwith speech dialog. The example in Table 3 illustrates that actionsignal generator 660 can generate action signals to automaticallycontrol residue distribution in the field being harvested.

TABLE 3 Human: “Johnny, spread residue to the right.” Operator InterfaceController: “Residue offset to the right 10 feet.”

The example shown in Table 4 illustrates that action signal generator160 can generate signals to control a residue subsystem in other waysthan table 3.

TABLE 4 Human: “Johnny, chop straw long for the next acre.” OperatorInterface Controller: “Next acre straw will be chopped long.”

Returning again to FIG. 10 , block 906 illustrates that operatorinterface controller 231 can detect and process conditions foroutputting a message or other information in other ways as well. Forinstance, other controller interaction system 656 can detect inputs fromother controllers indicating that alerts or output messages should bepresented to operator 260. Block 908 shows that the outputs can be audiomessages. Block 910 shows that the outputs can be visual messages, andblock 912 shows that the outputs can be haptic messages. Until operatorinterface controller 231 determines that the current harvestingoperation is completed, as indicated by block 914, processing reverts toblock 698 where the geographic location of harvester 100 is updated andprocessing proceeds as described above to update user interface display720.

Once the operation is complete, then any desired values that aredisplayed, or have been displayed on user interface display 720, can besaved. Those values can also be used in machine learning to improvedifferent portions of predictive model generator 210, predictive mapgenerator 212, control zone generator 213, control algorithms, or otheritems. Saving the desired values is indicated by block 916. The valuescan be saved locally on agricultural harvester 100, or the values can besaved at a remote server location or sent to another remote system.

It can thus be seen that an information map is obtained by anagricultural harvester and shows vegetative index values, moisturevalues, and topographic values at different geographic locations of afield being harvested. An in-situ sensor on the harvester senses aresidue characteristic as the agricultural harvester moves through thefield. A predictive map generator generates a predictive map thatpredicts control values, which can be values of a residue characteristicin some examples, for different locations in the field based on thevalues in the information map and the residue characteristic sensed bythe in-situ sensor. A control system controls controllable subsystembased on the control values in the predictive map.

A control value is a value upon which an action can be based. A controlvalue, as described herein, can include any value (or characteristicsindicated by or derived from the value) that may be used in the controlof agricultural harvester 100. A control value can be any valueindicative of an agricultural characteristic. A control value can be apredicted value, a measured value, or a detected value. A control valuemay include any of the values provided by a map, such as any of the mapsdescribed herein, for instance, a control value can be a value providedby an information map, a value provided by prior information map, or avalue provided predictive map, such as a functional predictive map. Acontrol value can also include any of the characteristics indicated byor derived from the values detected by any of the sensors describedherein. In other examples, a control value can be provided by anoperator of the agricultural machine, such as a command input by anoperator of the agricultural machine.

The present discussion has mentioned processors and servers. In someexamples, the processors and servers include computer processors withassociated memory and timing circuitry, not separately shown. Theprocessors and servers are functional parts of the systems or devices towhich the processors and servers belong and are activated by andfacilitate the functionality of the other components or items in thosesystems.

Also, a number of user interface displays have been discussed. Thedisplays can take a wide variety of different forms and can have a widevariety of different user actuatable operator interface mechanismsdisposed thereon. For instance, user actuatable operator interfacemechanisms may include text boxes, check boxes, icons, links, drop-downmenus, search boxes, etc. The user actuatable operator interfacemechanisms can also be actuated in a wide variety of different ways. Forinstance, the user actuatable operator interface mechanisms can beactuated using operator interface mechanisms such as a point and clickdevice, such as a track ball or mouse, hardware buttons, switches, ajoystick or keyboard, thumb switches or thumb pads, etc., a virtualkeyboard or other virtual actuators. In addition, where the screen onwhich the user actuatable operator interface mechanisms are displayed isa touch sensitive screen, the user actuatable operator interfacemechanisms can be actuated using touch gestures. Also, user actuatableoperator interface mechanisms can be actuated using speech commandsusing speech recognition functionality. Speech recognition may beimplemented using a speech detection device, such as a microphone, andsoftware that functions to recognize detected speech and executecommands based on the received speech.

A number of data stores have also been discussed. It will be noted thedata stores can each be broken into multiple data stores. In someexamples, one or more of the data stores may be local to the systemsaccessing the data stores, one or more of the data stores may all belocated remote form a system utilizing the data store, or one or moredata stores may be local while others are remote. All of theseconfigurations are contemplated by the present disclosure.

Also, the figures show a number of blocks with functionality ascribed toeach block. It will be noted that fewer blocks can be used to illustratethat the functionality ascribed to multiple different blocks isperformed by fewer components. Also, more blocks can be usedillustrating that the functionality may be distributed among morecomponents. In different examples, some functionality may be added, andsome may be removed.

It will be noted that the above discussion has described a variety ofdifferent systems, components, logic, and interactions. It will beappreciated that any or all of such systems, components, logic andinteractions may be implemented by hardware items, such as processors,memory, or other processing components, including but not limited toartificial intelligence components, such as neural networks, some ofwhich are described below, that perform the functions associated withthose systems, components, logic, or interactions. In addition, any orall of the systems, components, logic and interactions may beimplemented by software that is loaded into a memory and is subsequentlyexecuted by a processor or server or other computing component, asdescribed below. Any or all of the systems, components, logic andinteractions may also be implemented by different combinations ofhardware, software, firmware, etc., some examples of which are describedbelow. These are some examples of different structures that may be usedto implement any or all of the systems, components, logic andinteractions described above. Other structures may be used as well.

FIG. 12 is a block diagram of agricultural harvester 600, which may besimilar to agricultural harvester 100 shown in FIG. 2 . The agriculturalharvester 600 communicates with elements in a remote server architecture500. In some examples, remote server architecture 500 providescomputation, software, data access, and storage services that do notrequire end-user knowledge of the physical location or configuration ofthe system that delivers the services. In various examples, remoteservers may deliver the services over a wide area network, such as theinternet, using appropriate protocols. For instance, remote servers maydeliver applications over a wide area network and may be accessiblethrough a web browser or any other computing component. Software orcomponents shown in FIG. 2 as well as data associated therewith, may bestored on servers at a remote location. The computing resources in aremote server environment may be consolidated at a remote data centerlocation, or the computing resources may be dispersed to a plurality ofremote data centers. Remote server infrastructures may deliver servicesthrough shared data centers, even though the services appear as a singlepoint of access for the user. Thus, the components and functionsdescribed herein may be provided from a remote server at a remotelocation using a remote server architecture. Alternatively, thecomponents and functions may be provided from a server, or thecomponents and functions can be installed on client devices directly, orin other ways.

In the example shown in FIG. 12 , some items are similar to those shownin FIG. 2 and those items are similarly numbered. FIG. 12 specificallyshows that predictive model generator 210 or predictive map generator212, or both, may be located at a server location 502 that is remotefrom the agricultural harvester 600. Therefore, in the example shown inFIG. 12 , agricultural harvester 600 accesses systems through remoteserver location 502.

FIG. 12 also depicts another example of a remote server architecture.FIG. 12 shows that some elements of FIG. 2 may be disposed at a remoteserver location 502 while others may be located elsewhere. By way ofexample, data store 202 may be disposed at a location separate fromlocation 502 and accessed via the remote server at location 502.Regardless of where the elements are located, the elements can beaccessed directly by agricultural harvester 600 through a network suchas a wide area network or a local area network; the elements can behosted at a remote site by a service; or the elements can be provided asa service or accessed by a connection service that resides in a remotelocation. Also, data may be stored in any location, and the stored datamay be accessed by, or forwarded to, operators, users, or systems. Forinstance, physical carriers may be used instead of, or in addition to,electromagnetic wave carriers. In some examples, where wirelesstelecommunication service coverage is poor or nonexistent, anothermachine, such as a fuel truck or other mobile machine or vehicle, mayhave an automated, semi-automated, or manual information collectionsystem. As the combine harvester 600 comes close to the machinecontaining the information collection system, such as a fuel truck priorto fueling, the information collection system collects the informationfrom the combine harvester 600 using any type of ad-hoc wirelessconnection. The collected information may then be forwarded to anothernetwork when the machine containing the received information reaches alocation where wireless telecommunication service coverage or otherwireless coverage—is available. For instance, a fuel truck may enter anarea having wireless communication coverage when traveling to a locationto fuel other machines or when at a main fuel storage location. All ofthese architectures are contemplated herein. Further, the informationmay be stored on the agricultural harvester 600 until the agriculturalharvester 600 enters an area having wireless communication coverage. Theagricultural harvester 600, itself, may send the information to anothernetwork.

It will also be noted that the elements of FIG. 2 , or portions thereof,may be disposed on a wide variety of different devices. One or more ofthose devices may include an on-board computer, an electronic controlunit, a display unit, a server, a desktop computer, a laptop computer, atablet computer, or other mobile device, such as a palm top computer, acell phone, a smart phone, a multimedia player, a personal digitalassistant, etc.

In some examples, remote server architecture 500 may includecybersecurity measures. Without limitation, these measures may includeencryption of data on storage devices, encryption of data sent betweennetwork nodes, authentication of people or processes accessing data, aswell as the use of ledgers for recording metadata, data, data transfers,data accesses, and data transformations. In some examples, the ledgersmay be distributed and immutable (e.g., implemented as blockchain).

FIG. 13 is a simplified block diagram of one illustrative example of ahandheld or mobile computing device that can be used as a user's orclient's hand held device 16, in which the present system (or parts ofit) can be deployed. For instance, a mobile device can be deployed inthe operator compartment of agricultural harvester 100 for use ingenerating, processing, or displaying the maps discussed above. FIGS.14-15 are examples of handheld or mobile devices.

FIG. 13 provides a general block diagram of the components of a clientdevice 16 that can run some components shown in FIG. 2 , that interactswith them, or both. In the device 16, a communications link 13 isprovided that allows the handheld device to communicate with othercomputing devices and under some examples provides a channel forreceiving information automatically, such as by scanning. Examples ofcommunications link 13 include allowing communication though one or morecommunication protocols, such as wireless services used to providecellular access to a network, as well as protocols that provide localwireless connections to networks.

In other examples, applications can be received on a removable SecureDigital (SD) card that is connected to an interface 15. Interface 15 andcommunication links 13 communicate with a processor 17 (which can alsoembody processors or servers from other FIGS.) along a bus 19 that isalso connected to memory 21 and input/output (I/O) components 23, aswell as clock 25 and location system 27.

I/O components 23, in one example, are provided to facilitate input andoutput operations. I/O components 23 for various examples of the device16 can include input components such as buttons, touch sensors, opticalsensors, microphones, touch screens, proximity sensors, accelerometers,orientation sensors and output components such as a display device, aspeaker, and or a printer port. Other I/O components 23 can be used aswell.

Clock 25 illustratively comprises a real time clock component thatoutputs a time and date. It can also, illustratively, provide timingfunctions for processor 17.

Location system 27 illustratively includes a component that outputs acurrent geographical location of device 16. This can include, forinstance, a global positioning system (GPS) receiver, a LORAN system, adead reckoning system, a cellular triangulation system, or otherpositioning system. Location system 27 can also include, for example,mapping software or navigation software that generates desired maps,navigation routes and other geographic functions.

Memory 21 stores operating system 29, network settings 31, applications33, application configuration settings 35, data store 37, communicationdrivers 39, and communication configuration settings 41. Memory 21 caninclude all types of tangible volatile and non-volatilecomputer-readable memory devices. Memory 21 may also include computerstorage media (described below). Memory 21 stores computer readableinstructions that, when executed by processor 17, cause the processor toperform computer-implemented steps or functions according to theinstructions. Processor 17 may be activated by other components tofacilitate their functionality as well.

FIG. 14 shows one example in which device 16 is a tablet computer 600.In FIG. 14 , computer 601 is shown with user interface display screen602. Screen 602 can be a touch screen or a pen-enabled interface thatreceives inputs from a pen or stylus. Tablet computer 600 may also usean on-screen virtual keyboard. Of course, computer 601 might also beattached to a keyboard or other user input device through a suitableattachment mechanism, such as a wireless link or USB port, for instance.Computer 601 may also illustratively receive voice inputs as well.

FIG. 15 is similar to FIG. 14 except that the device is a smart phone71. Smart phone 71 has a touch sensitive display 73 that displays iconsor tiles or other user input mechanisms 75. Mechanisms 75 can be used bya user to run applications, make calls, perform data transferoperations, etc. In general, smart phone 71 is built on a mobileoperating system and offers more advanced computing capability andconnectivity than a feature phone.

Note that other forms of the devices 16 are possible.

FIG. 16 is one example of a computing environment in which elements ofFIG. 2 can be deployed. With reference to FIG. 16 , an example systemfor implementing some embodiments includes a computing device in theform of a computer 810 programmed to operate as discussed above.Components of computer 810 may include, but are not limited to, aprocessing unit 820 (which can comprise processors or servers fromprevious FIGS.), a system memory 830, and a system bus 821 that couplesvarious system components including the system memory to the processingunit 820. The system bus 821 may be any of several types of busstructures including a memory bus or memory controller, a peripheralbus, and a local bus using any of a variety of bus architectures. Memoryand programs described with respect to FIG. 2 can be deployed incorresponding portions of FIG. 16 .

Computer 810 typically includes a variety of computer readable media.Computer readable media may be any available media that can be accessedby computer 810 and includes both volatile and nonvolatile media,removable and non-removable media. By way of example, and notlimitation, computer readable media may comprise computer storage mediaand communication media. Computer storage media is different from, anddoes not include, a modulated data signal or carrier wave. Computerreadable media includes hardware storage media including both volatileand nonvolatile, removable and non-removable media implemented in anymethod or technology for storage of information such as computerreadable instructions, data structures, program modules or other data.Computer storage media includes, but is not limited to, RAM, ROM,EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by computer 810. Communication media mayembody computer readable instructions, data structures, program modulesor other data in a transport mechanism and includes any informationdelivery media. The term “modulated data signal” means a signal that hasone or more of its characteristics set or changed in such a manner as toencode information in the signal.

The system memory 830 includes computer storage media in the form ofvolatile and/or nonvolatile memory or both such as read only memory(ROM) 831 and random access memory (RAM) 832. A basic input/outputsystem 833 (BIOS), containing the basic routines that help to transferinformation between elements within computer 810, such as duringstart-up, is typically stored in ROM 831. RAM 832 typically containsdata or program modules or both that are immediately accessible toand/or presently being operated on by processing unit 820. By way ofexample, and not limitation, FIG. 16 illustrates operating system 834,application programs 835, other program modules 836, and program data837.

The computer 810 may also include other removable/non-removablevolatile/nonvolatile computer storage media. By way of example only,FIG. 16 illustrates a hard disk drive 841 that reads from or writes tonon-removable, nonvolatile magnetic media, an optical disk drive 855,and nonvolatile optical disk 856. The hard disk drive 841 is typicallyconnected to the system bus 821 through a non-removable memory interfacesuch as interface 840, and optical disk drive 855 are typicallyconnected to the system bus 821 by a removable memory interface, such asinterface 850.

Alternatively, or in addition, the functionality described herein can beperformed, at least in part, by one or more hardware logic components.For example, and without limitation, illustrative types of hardwarelogic components that can be used include Field-programmable Gate Arrays(FPGAs), Application-specific Integrated Circuits (e.g., ASICs),Application-specific Standard Products (e.g., ASSPs), System-on-a-chipsystems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.

The drives and their associated computer storage media discussed aboveand illustrated in FIG. 16 , provide storage of computer readableinstructions, data structures, program modules and other data for thecomputer 810. In FIG. 16 , for example, hard disk drive 841 isillustrated as storing operating system 844, application programs 845,other program modules 846, and program data 847. Note that thesecomponents can either be the same as or different from operating system834, application programs 835, other program modules 836, and programdata 837.

A user may enter commands and information into the computer 810 throughinput devices such as a keyboard 862, a microphone 863, and a pointingdevice 861, such as a mouse, trackball or touch pad. Other input devices(not shown) may include a joystick, game pad, satellite dish, scanner,or the like. These and other input devices are often connected to theprocessing unit 820 through a user input interface 860 that is coupledto the system bus, but may be connected by other interface and busstructures. A visual display 891 or other type of display device is alsoconnected to the system bus 821 via an interface, such as a videointerface 890. In addition to the monitor, computers may also includeother peripheral output devices such as speakers 897 and printer 896,which may be connected through an output peripheral interface 895.

The computer 810 is operated in a networked environment using logicalconnections (such as a controller area network—CAN, local areanetwork—LAN, or wide area network WAN) to one or more remote computers,such as a remote computer 880.

When used in a LAN networking environment, the computer 810 is connectedto the LAN 871 through a network interface or adapter 870. When used ina WAN networking environment, the computer 810 typically includes amodem 872 or other means for establishing communications over the WAN873, such as the Internet. In a networked environment, program modulesmay be stored in a remote memory storage device. FIG. 16 illustrates,for example, that remote application programs 885 can reside on remotecomputer 880.

It should also be noted that the different examples described herein canbe combined in different ways. That is, parts of one or more examplescan be combined with parts of one or more other examples. All of this iscontemplated herein.

Example 1 is an agricultural work machine comprising

a communication system that receives an information map that includesvalues of an agricultural characteristic corresponding to differentgeographic locations in a field;

a geographic position sensor that detects a geographic location of theagricultural work machine;

an in-situ sensor that detects a value of a residue characteristiccorresponding to the geographic location;

a predictive map generator that generates a functional predictiveagricultural map of the field that maps predictive values of the residuecharacteristic to the different geographic locations in the field basedon the values of the agricultural characteristic in the information mapand based on the value of the residue characteristic;

a controllable subsystem; and

a control system that generates a control signal to control thecontrollable subsystem based on the geographic position of theagricultural work machine and based on the predictive values of theresidue characteristic in the functional predictive agricultural map.

Example 2 is the agricultural work machine of any or all previousexamples, wherein the predictive map generator comprises:

a predictive residue subsystem characteristic map generator thatgenerates a functional predictive residue subsystem characteristic mapthat maps predictions of a residue subsystem characteristic to thedifferent geographic locations in the field.

Example 3 is the agricultural work machine of any or all previousexamples, wherein the control system comprises:

a residue subsystem controller that generates a residue subsystemcontrol signal, based on the geographic location and the functionalpredictive residue subsystem characteristic map, and controls a residuesubsystem as the controllable subsystem based on the residue subsystemcontrol signal.

Example 4 is the agricultural work machine of any or all previousexamples, wherein the residue subsystem controller controls, a residuespreader of the residue subsystem based on the residue subsystem controlsignal.

Example 5 is the agricultural work machine of any or all previousexamples, wherein the information map comprises a vegetative index mapthat maps, as the agricultural characteristic, vegetative index valuesto the different geographic locations in the field.

Example 6 is the agricultural work machine of any or all previousexamples, wherein the information map comprises a moisture map thatmaps, as the agricultural characteristic, moisture values to thedifferent geographic locations in the field.

Example 7 is the agricultural work machine of any or all previousexamples, wherein the information map comprises a topographic map thatmaps, as the agricultural characteristic, topographic characteristicvalues to the different geographic locations in the field.

Example 8 is the agricultural work machine of any or all previousexamples wherein the residue characteristic comprises a residue spreadin one or more dimensions.

Example 9 is the agricultural work machine of any or all previousexamples wherein the residue characteristic comprises residueuniformity.

Example 10 is the agricultural work machine of any or all previousexamples wherein the control system further comprises:

an operator interface controller that generates a user interface maprepresentation of the functional predictive agricultural map, the userinterface map representation comprising a field portion with one or moremarkers indicating the predictive values of the residue characteristicat one or more geographic locations on the field portion.

Example 11 is a computer implemented method of controlling anagricultural work machine comprising

obtaining an information map that includes values of an agriculturalcharacteristic corresponding to different geographic locations in afield;

detecting a geographic location of the agricultural work machine;

detecting, with an in-situ sensor, a value of a residue characteristiccorresponding to a geographic location;

generating a functional predictive agricultural map of the field thatmaps predictive control values to the different geographic locations inthe field based on the values of the agricultural characteristic in theinformation map and based on the value of the residue characteristiccorresponding to the geographic location; and

controlling a controllable subsystem based on the geographic position ofthe agricultural work machine and based on the control values in thefunctional predictive agricultural map.

Example 12 is the computer implemented method of any or all previousexamples, wherein generating a functional predictive agricultural mapcomprises:

generating a functional predictive residue characteristic map that mapspredictive residue characteristics as the control values to thedifferent geographic locations in the field.

Example 13 is the computer implemented method of any or all previousexamples, wherein the functional predictive residue characteristic mapmaps predictive residue spread values as the predictive residuecharacteristics to the different geographic locations in the field.

Example 14 is the computer implemented method of any or all previousexamples, wherein controlling a controllable subsystem comprises:

generating a residue control signal based on the detected geographiclocation and the functional predictive residue characteristic map; and

controlling the controllable subsystem based on the residue controlsignal to control a residue spreader of the agricultural work machine.

Example 15 is the computer implemented method of any or all previousexamples, wherein the functional predictive residue characteristic mapmaps predictive residue content values as residue characteristic to thedifferent geographic locations in the field.

Example 16 is the computer implemented method of any or all previousexamples, wherein controlling a controllable subsystem comprises:

generating a residue control signal based on the detected geographiclocation and the functional predictive residue characteristic map; and

controlling the controllable subsystem based on the residue controlsignal to control a residue chopper of the agricultural work machine.

Example 17 is the computer implemented method of any or all previousexamples, wherein the functional predictive residue characteristic mapmaps predictive residue uniformity values as the predictive residuecharacteristics to the different geographic locations in the field.

Example 18 is the computer implemented method of any or all previousexamples, wherein controlling a controllable subsystem comprises:

generating a residue control signal based on the detected geographiclocation and the functional predictive residue characteristic map; and

controlling the controllable subsystem based on the residue controlsignal.

Example 19 is an agricultural work machine comprising:

a communication system that receives an information map that includesvalues of an agricultural characteristic corresponding to differentgeographic locations in a field;

a geographic position sensor that detects a geographic location of theagricultural work machine;

an in-situ sensor that detects a value of a residue characteristiccorresponding to a geographic location;

a predictive model generator that generates a predictive agriculturalmodel that models a relationship between the agricultural characteristicand the residue characteristic based on a value of the agriculturalcharacteristic in the information map at the geographic location and avalue of the residue characteristic detected by the in-situ sensor atthe geographic location;

a predictive map generator that generates a functional predictiveagricultural map of the field that maps predictive control values to thedifferent geographic locations in the field based on the values of theagricultural characteristic in the information map and based on thepredictive agricultural model;

a controllable subsystem; and

a control system that generates a control signal to control thecontrollable subsystem based on the geographic position of theagricultural work machine and based on the control values in thefunctional predictive agricultural map.

Example 20 is the agricultural work machine of any or all previousexamples, wherein the control system comprises:

a residue controller that generates a residue control signal based onthe detected geographic location and the functional predictiveagricultural map and controls one or more of a residue chopper, aresidue spreader, and a seed eliminator as the controllable subsystembased on the residue control signal.

Although the subject matter has been described in language specific tostructural features or methodological acts, it is to be understood thatthe subject matter defined in the appended claims is not necessarilylimited to the specific features or acts described above. Rather, thespecific features and acts described above are disclosed as exampleforms of the claims.

What is claimed is:
 1. An agricultural work machine comprising acommunication system that receives an information map that includesvalues of an agricultural characteristic corresponding to differentgeographic locations in a field; a geographic position sensor thatdetects a geographic location of the agricultural work machine; anin-situ sensor that detects a value of a residue characteristiccorresponding to the geographic location; a predictive map generatorthat generates a functional predictive agricultural map of the fieldthat maps predictive values of the residue characteristic to thedifferent geographic locations in the field based on the values of theagricultural characteristic in the information map and based on thevalue of the residue characteristic; a controllable subsystem; and acontrol system that generates a control signal to control thecontrollable subsystem based on the geographic position of theagricultural work machine and based on the predictive values of theresidue characteristic in the functional predictive agricultural map. 2.The agricultural work machine of claim 1, wherein the predictive mapgenerator comprises: a predictive residue subsystem characteristic mapgenerator that generates a functional predictive residue subsystemcharacteristic map that maps predictions of a residue subsystemcharacteristic to the different geographic locations in the field. 3.The agricultural work machine of claim 2, wherein the control systemcomprises: a residue subsystem controller that generates a residuesubsystem control signal, based on the geographic location and thefunctional predictive residue subsystem characteristic map, and controlsa residue subsystem as the controllable subsystem based on the residuesubsystem control signal.
 4. The agricultural work machine of claim 3,wherein the residue subsystem controller controls, a residue spreader ofthe residue subsystem based on the residue subsystem control signal. 5.The agricultural work machine of claim 1, wherein the information mapcomprises a vegetative index map that maps, as the agriculturalcharacteristic, vegetative index values to the different geographiclocations in the field.
 6. The agricultural work machine of claim 1,wherein the information map comprises a moisture map that maps, as theagricultural characteristic, moisture values to the different geographiclocations in the field.
 7. The agricultural work machine of claim 1,wherein the information map comprises a topographic map that maps, asthe agricultural characteristic, topographic characteristic values tothe different geographic locations in the field.
 8. The agriculturalwork machine of claim 1 wherein the residue characteristic comprises aresidue spread in one or more dimensions.
 9. The agricultural workmachine of claim 1 wherein the residue characteristic comprises residueuniformity.
 10. The agricultural work machine of claim 1 wherein thecontrol system further comprises: an operator interface controller thatgenerates a user interface map representation of the functionalpredictive agricultural map, the user interface map representationcomprising a field portion with one or more markers indicating thepredictive values of the residue characteristic at one or moregeographic locations on the field portion.